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Sun, Yanxia. "Improved particle swarm optimisation algorithms." Thesis, Paris Est, 2011. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1000395.
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D. Tech. Electrical Engineering.<br>Particle Swarm Optimisation (PSO) is based on a metaphor of social interaction such as birds flocking or fish schooling to search a space by adjusting the trajectories of individual vectors, called "particles" conceptualized as moving points in a multidimensional space. This thesis presents several algorithms/techniques to improve the PSO's global search ability. Simulation and analytical results confirm the efficiency of the proposed algorithms/techniques when compared to the other state of the art algorithms.
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Kelman, Alexander. "Utilizing Swarm Intelligence Algorithms for Pathfinding in Games." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636.
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The Ant Colony Optimization and Particle Swarm Optimization are two Swarm Intelligence algorithms often utilized for optimization. Swarm Intelligence relies on agents that possess fragmented knowledge, a concept not often utilized in games. The aim of this study is to research whether there are any benefits to using these Swarm Intelligence algorithms in comparison to standard algorithms such as A* for pathfinding in a game. Games often consist of dynamic environments with mobile agents, as such all experiments were conducted with dynamic destinations. Algorithms were measured on the length of their path and the time taken to calculate that path. The algorithms were implemented with minor modifications to allow them to better function in a grid based environment. The Ant Colony Optimization was modified in regards to how pheromone was distributed in the dynamic environment to better allow the algorithm to path towards a mobile target. Whereas the Particle Swarm Optimization was given set start positions and velocity in order to increase initial search space and modifications to increase particle diversity. The results obtained from the experimentation showcased that the Swarm Intelligence algorithms were capable of performing to great results in terms of calculation speed, they were however not able to obtain the same path optimality as A*. The algorithms' implementation can be improved but show potential to be useful in games.
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Kassabalidis, Ioannis N. "Applications of biologically inspired algorithms to complex systems /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/5915.
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Luitel, Bipul. "Applications of swarm, evolutionary and quantum algorithms in system identification and digital filter design." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2009. http://scholarsmine.mst.edu/thesis/pdf/Luitel_09007dcc805cd792.pdf.
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Thesis (M.S.)--Missouri University of Science and Technology, 2009.<br>Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed January 22, 2009) Includes bibliographical references (p. 135-137).
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Hernández, Pibernat Hugo. "Swarm intelligence techniques for optimization and management tasks insensor networks." Doctoral thesis, Universitat Politècnica de Catalunya, 2012. http://hdl.handle.net/10803/81861.
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The main contributions of this thesis are located in the domain of wireless sensor netorks. More in detail, we introduce energyaware
algorithms and protocols in the context of the following topics: self-synchronized duty-cycling in networks with energy
harvesting capabilities, distributed graph coloring and minimum energy broadcasting with realistic antennas. In the following, we
review the research conducted in each case.
We propose a self-synchronized duty-cycling mechanism for sensor networks. This mechanism is based on the working and resting
phases of natural ant colonies, which show self-synchronized activity phases. The main goal of duty-cycling methods is to save
energy by efficiently alternating between different states. In the case at hand, we considered two different states: the sleep state,
where communications are not possible and energy consumption is low; and the active state, where communication result in a
higher energy consumption.
In order to test the model, we conducted an extensive experimentation with synchronous simulations on mobile networks and static
networks, and also considering asynchronous networks. Later, we extended this work by assuming a broader point of view and
including a comprehensive study of the parameters. In addition, thanks to a collaboration with the Technical University of
Braunschweig, we were able to test our algorithm in the real sensor network simulator Shawn (http://shawn.sf.net).
The second part of this thesis is devoted to the desynchronization of wireless sensor nodes and its application to the distributed
graph coloring problem. In particular, our research is inspired by the calling behavior of Japanese tree frogs, whose males use their
calls to attract females. Interestingly, as female frogs are only able to correctly localize the male frogs when their calls are not too
close in time, groups of males that are located nearby each other desynchronize their calls.
Based on a model of this behavior from the literature, we propose a novel algorithm with applications to the field of sensor
networks. More in detail, we analyzed the ability of the algorithm to desynchronize neighboring nodes. Furthermore, we considered
extensions of the original model, hereby improving its desynchronization capabilities.To illustrate the potential benefits of
desynchronized networks, we then focused on distributed graph coloring. Later, we analyzed the algorithm more extensively and
show its performance on a larger set of benchmark instances.
The classical minimum energy broadcast (MEB) problem in wireless ad hoc networks, which is well-studied in the scientific
literature, considers an antenna model that allows the adjustment of the transmission power to any desired real value from zero up
to the maximum transmission power level. However, when specifically considering sensor networks, a look at the currently
available hardware shows that this antenna model is not very realistic. In this work we re-formulate the MEB problem for an
antenna model that is realistic for sensor networks. In this antenna model transmission power levels are chosen from a finite set of
possible ones. A further contribution concerns the adaptation of an ant colony optimization algorithm --currently being the state of
the art for the classical MEB problem-- to the more realistic problem version, the so-called minimum energy broadcast problem with
realistic antennas (MEBRA). The obtained results show that the advantage of ant colony optimization over classical heuristics even
grows when the number of possible transmission power levels decreases. Finally we build a distributed version of the algorithm,
which also compares quite favorably against centralized heuristics from the literature.<br>Las principles contribuciones de esta tesis se encuentran en el domino de las redes de sensores inalámbricas. Más en detalle, introducimos algoritmos y protocolos que intentan minimizar el consumo energético para los siguientes problemas: gestión autosincronizada de encendido y apagado de sensores con capacidad para obtener energía del ambiente, coloreado de grafos distribuido y broadcasting de consumo mínimo en entornos con antenas reales.
En primer lugar, proponemos un sistema capaz de autosincronizar los ciclos de encendido y apagado de los nodos de una red de sensores. El mecanismo está basado en las fases de trabajo y reposo de las colonias de hormigas tal y como estas pueden observarse en la naturaleza, es decir, con fases de actividad autosincronizadas. El principal objectivo de este tipo de técnicas es ahorrar energía gracias a alternar estados de forma eficiente. En este caso en concreto, consideramos dos estados diferentes: el estado dormido, en el que los nodos no pueden comunicarse y el consumo energético es bajo; y el estado activo, en el que las comunicaciones propician un consumo energético elevado.
Con el objetivo de probar el modelo, se ha llevado a cabo una extensa experimentación que incluye tanto simulaciones síncronas en redes móviles y estáticas, como simulaciones en redes asíncronas. Además, este trabajo se extendió asumiendo un punto de vista más amplio e incluyendo un detallado estudio de los parámetros del algoritmo. Finalmente, gracias a la colaboración con la Technical University of Braunschweig, tuvimos la oportunidad de probar el mecanismo en el simulador realista de redes de sensores, Shawn (http://shawn.sf.net).
La segunda parte de esta tesis está dedicada a la desincronización de nodos en redes de sensores y a su aplicación al problema del coloreado de grafos de forma distribuida. En particular, nuestra investigación está inspirada por el canto de las ranas de árbol japonesas, cuyos machos utilizan su canto para atraer a las hembras. Resulta interesante que debido a que las hembras solo son capaces de localizar las ranas macho cuando sus cantos no están demasiado cerca en el tiempo, los grupos de machos que se hallan en una misma región desincronizan sus cantos.
Basado en un modelo de este comportamiento que se encuentra en la literatura, proponemos un nuevo algoritmo con aplicaciones al campo de las redes de sensores. Más en detalle, analizamos la habilidad del algoritmo para desincronizar nodos vecinos. Además, consideramos extensiones del modelo original, mejorando su capacidad de desincronización. Para ilustrar los potenciales beneficios de las redes desincronizadas, nos centramos en el problema del coloreado de grafos distribuido que tiene relación con diferentes tareas habituales en redes de sensores.
El clásico problema del broadcasting de consumo mínimo en redes ad hoc ha sido bien estudiado en la literatura. El problema considera un modelo de antena que permite transmitir a cualquier potencia elegida (hasta un máximo establecido por el dispositivo). Sin embargo, cuando se trabaja de forma específica con redes de sensores, un vistazo al hardware actualmente disponible muestra que este modelo de antena no es demasiado realista. En este trabajo reformulamos el problema para el modelo de antena más habitual en redes de sensores. En este modelo, los niveles de potencia de transmisión se eligen de un conjunto finito de posibilidades. La siguiente contribución consiste en en la adaptación de un algoritmo de optimización por colonias de hormigas a la versión más realista del problema, también conocida como broadcasting de consumo mínimo con antenas realistas.
Los resultados obtenidos muestran que la ventaja de este método sobre heurísticas clásicas incluso crece cuando el número de posibles potencias de transmisión decrece. Además, se ha presentado una versión distribuida del algoritmo, que también se compara de forma bastante favorable contra las heurísticas centralizadas conocidas.
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Brits, Riaan. "Niching strategies for particle swarm optimization." Diss., Pretoria : [s.n.], 2002. http://upetd.up.ac.za/thesis/available/etd-02192004-143003.
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Liu, Fang. "Nature inspired computational intelligence for financial contagion modelling." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/8208.
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Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market.
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Khoury, Pascal. "Traditional and swarm intelligence based algorithms for stock selection and risk modelling in emerging markets." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10042631/.
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Muthuswamy, Shanthi. "Discrete particle swarm optimization algorithms for orienteering and team orienteering problems." Diss., Online access via UMI:, 2009.
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Al-Obaidi, Mohanad. "ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence." Thesis, De Montfort University, 2010. http://hdl.handle.net/2086/5187.
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Although traditionally Wireless Sensor Network (WSNs) have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm shift from static to more dynamic environments, where nodes are attached to moving objects, people or animals. Applications that use WSNs in motion are broad, ranging from transport and logistics to animal monitoring, health care and military. These application domains have a number of characteristics that challenge the algorithmic design of WSNs. Firstly, mobility has a negative effect on the quality of the wireless communication and the performance of networking protocols. Nevertheless, it has been shown that mobility can enhance the functionality of the network by exploiting the movement patterns of mobile objects. Secondly, the heterogeneity of devices in a WSN has to be taken into account for increasing the network performance and lifetime. Thirdly, the WSN services should ideally assist the user in an unobtrusive and transparent way. Fourthly, energy-efficiency and scalability are of primary importance to prevent the network performance degradation. This thesis contributes toward the design of a new hybrid optimization algorithm; ENAMS (Energy optimizatioN Algorithm for Mobile Sensor networks) which is based on the Evolutionary Computation and Swarm Intelligence to increase the life time of mobile wireless sensor networks. The presented algorithm is suitable for large scale mobile sensor networks and provides a robust and energy- efficient communication mechanism by dividing the sensor-nodes into clusters, where the number of clusters is not predefined and the sensors within each cluster are not necessary to be distributed in the same density. The presented algorithm enables the sensor nodes to move as swarms within the search space while keeping optimum distances between the sensors. To verify the objectives of the proposed algorithm, the LEGO-NXT MIND-STORMS robots are used to act as particles in a moving swarm keeping the optimum distances while tracking each other within the permitted distance range in the search space.
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Labella, Thomas Halva. "Division of labour in groups of robots." Doctoral thesis, Universite Libre de Bruxelles, 2007. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210738.
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In this thesis, we examine algorithms for the division of labour in a group of robot. The algorithms make no use of direct communication. Instead, they are based only on the interactions among the robots and between the group and the environment.<p><p>Division of labour is the mechanism that decides how many robots shall be used to perform a task. The efficiency of the group of robots depends in fact on the number of robots involved in a task. If too few robots are used to achieve a task, they might not be successful or might perform poorly. If too many robots are used, it might be a waste of resources. The number of robots to use might be decided a priori by the system designer. More interestingly, the group of robots might autonomously select how many and which robots to use. In this thesis, we study algorithms of the latter type.<p><p>The robotic literature offers already some solutions, but most of them use a form of direct communication between agents. Direct, or explicit, communication between the robots is usually considered a necessary condition for co-ordination. Recent studies have questioned this assumption. The claim is based on observations of animal colonies, e.g. ants and termites. They can effectively co-operate without directly communicating, but using indirect forms of communication like stigmergy. Because they do not rely on communication, such colonies show robust behaviours at group level, a condition that one wishes also for groups of robots. Algorithms for robot co-ordination without direct communication have been proposed in the last few years. They are interesting not only because they are a stimulating intellectual challenge, but also because they address a situation that might likely occur when using robots for real-world out-door applications. Unfortunately, they are still poorly studied.<p><p>This thesis helps the understanding and the development of such algorithms. We start from a specific case to learn its characteristics. Then we improve our understandings through comparisons with other solutions, and finally we port everything into another domain.<p><p>We first study an algorithm for division of labour that was inspired by ants' foraging. We test the algorithm in an application similar to ants' foraging: prey retrieval. We prove that the model used for ants' foraging can be effective also in real conditions. Our analysis allows us to understand the underlying mechanisms of the division of labour and to define some way of measuring it.<p><p>Using this knowledge, we continue by comparing the ant-inspired algorithm with similar solutions that can be found in the literature and by assessing their differences. In performing these comparisons, we take care of using a formal methodology that allows us to spare resources. Namely, we use concepts of experiment design to reduce the number of experiments with real robots, without losing significance in the results.<p><p>Finally, we apply and port what we previously learnt into another application: Sensor/Actor Networks (SANETs). We develop an architecture for division of labour that is based on the same mechanisms as the ants' foraging model. Although the individuals in the SANET can communicate, the communication channel might be overloaded. Therefore, the agents of a SANET shall be able to co-ordinate without accessing the communication channel.<br>Doctorat en sciences appliquées<br>info:eu-repo/semantics/nonPublished
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Pěnčík, Martin. "Plánování cesty robotu pomocí mravenčích algoritmů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-231644.
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This thesis deals with robot path planning. It contains an overview of general approaches for path planning and describes methods of swarm intelligence and their application for robot path planning. This paper also contains proposals of adjustments for ant algorithms and it presents experimental results of algorithm implementation.
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Khan, Salman A. "Design and analysis of evolutionary and swarm intelligence techniques for topology design of distributed local area networks." Pretori: [S.n.], 2009. http://upetd.up.ac.za/thesis/available/etd-09272009-153908/.
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Morcos, Karim M. "Genetic network parameter estimation using single and multi-objective particle swarm optimization." Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/9207.
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Master of Science<br>Department of Electrical and Computer Engineering<br>Sanjoy Das<br>Stephen M. Welch<br>Multi-objective optimization problems deal with finding a set of candidate optimal solutions to be presented to the decision maker. In industry, this could be the problem of finding alternative car designs given the usually conflicting objectives of performance, safety, environmental friendliness, ease of maintenance, price among others. Despite the significance of this problem, most of the non-evolutionary algorithms which are widely used cannot find a set of diverse and nearly optimal solutions due to the huge size of the search space. At the same time, the solution set produced by most of the currently used evolutionary algorithms lacks diversity.
The present study investigates a new optimization method to solve multi-objective problems based on the widely used swarm-intelligence approach, Particle Swarm Optimization (PSO). Compared to other approaches, the proposed algorithm converges relatively fast while maintaining a diverse set of solutions. The investigated algorithm, Partially Informed Fuzzy-Dominance (PIFD) based PSO uses a dynamic network topology and fuzzy dominance to guide the swarm of dominated solutions.
The proposed algorithm in this study has been tested on four benchmark problems and other real-world applications to ensure proper functionality and assess overall performance. The multi-objective gene regulatory network (GRN) problem entails the minimization of the coefficient of variation of modified photothermal units (MPTUs) across multiple sites along with the total sum of similarity background between ecotypes. The results throughout the current research study show that the investigated algorithm attains outstanding performance regarding optimization aspects, and exhibits rapid convergence and diversity.
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RIBEIRO, Tiago Martins. "Desenvolvimento de método de inteligência artificial baseado no comportamento de enxames do gafanhoto-do-deserto." Universidade Federal do Maranhão, 2017. http://tedebc.ufma.br:8080/jspui/handle/tede/1294.
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Previous issue date: 2017-02-20<br>CAPES<br>Complex optimization problems have been studied over the years by researchers seeking
better solutions, these studies have encouraged the development of several algorithms of
artificial intelligence, and a part of them are bio-inspired methods, based on the behavior of
populations. These algorithms target to develop techniques based on nature in search of
solutions to these problems. In this work, was introduced as a purpose, an algorithm based
on the behavior of locust swarms, the Locust Swarm Optimizer (LSO). The behavior of the
desert locust is introduced highlighting the formation of clouds of attacks caused by a
synthesized neurotransmitter monoamine, present on the insect, known as serotonin.
Observing this behavior, the LSO was developed. It was compared to other known
artificial intelligence techniques through 23 benchmark functions and also tested on an
power system economical dispatch problem. From the point of view of the results and the
ease of implementation, it can be concluded that the LSO algorithm is very competitive as
compared to existing methods<br>Problemas complexos de otimização vêm sendo estudados ao longo dos anos por
pesquisadores que buscam melhores soluções, estes estudos incentivaram o
desenvolvimento de vários algoritmos de inteligência artificial, sendo que uma parte deles
são métodos bioinspirados, baseados no comportamento de populações. Estes algoritmos
têm como objetivo desenvolver técnicas baseadas na natureza em busca de soluções para
estes problemas. Neste trabalho um algoritmo baseado no comportamento de enxames de
gafanhotos-do-deserto, o Locust Swarm Optimizer (LSO), foi introduzido como objetivo.
O comportamento do gafanhoto-do-deserto é apresentado destacando a formação de
nuvens de ataques causada por uma monoamina neurotransmissora sintetizada, presente no
inseto, conhecido por serotonina. Observando este comportamento, foi desenvolvido o
LSO. Ele foi comparado com outras conhecidas técnicas de inteligência artificial através
de 23 funções benchmarks e também, testado em um problema de despacho econômico.
Do ponto de vista dos resultados e da facilidade de implementação, pode-se concluir que o
algoritmo LSO é bastante competitivo comparado aos métodos atuais existentes.
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Cruz, Dávila Patrícia Ferreira. "Agrupamento e classificação de dados utilizando um algoritmo inspirado no comportamento de abelhas." Universidade Presbiteriana Mackenzie, 2015. http://tede.mackenzie.br/jspui/handle/tede/1463.
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Previous issue date: 2015-06-17<br>With the popularization of Internet, the advancement of electronic devices and the ease of storage, the volume of data stored and available at companies has increased substantially. Therefore, it becomes necessary to use intelligent techniques to extract useful information and knowledge from these data. In this context, Data Mining has been the aim of several researches by providing a set of intelligent techniques to the exploration of large volumes of data. The present project aims to research and develop new algorithms inspired by the collective behavior of bee colonies for solving complex clustering and classification tasks. More specifically, this project proposes adaptations of an optimization algorithm inspired by the behavior of bees so that it can be applied to solve clustering problems and also for positioning centers of RBF neural networks. The proposed approaches were applied to several benchmark problems with promising results.<br>Com a popularização da Internet, o avanço dos dispositivos eletrônicos e a facilidade de armazenamento, o volume de dados armazenados e disponibilizados por empresas de diversos ramos tem aumentado rapidamente. Com isso, torna-se necessária a utilização de técnicas avançadas capazes de extrair desses dados informações úteis e conhecimentos que, na maioria das vezes, estão implícitos. Nesse contexto, a Mineração de Dados tem sido alvo de diversas pesquisas por prover um conjunto de técnicas inteligentes para a exploração de grandes volumes de dados. O presente projeto visa à investigação e desenvolvimento de novos algoritmos inspirados no comportamento coletivo das colônias de abelhas para aplicação em problemas complexos de classificação e agrupamentos de dados, que são importantes tarefas da Mineração de Dados. Mais especificamente, esse projeto propõe adaptações de um algoritmo de otimização inspirado no comportamento de abelhas, sua aplicação em problemas de agrupamento de dados e para o posicionamento de centros de redes neurais do tipo RBF. Os resultados experimentais em bases de dados da literatura mostraram a viabilidade e benefícios das propostas, tanto para problemas de agrupamento, quanto para problemas de classificação.
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Noudjiep, Djiepkop Giresse Franck. "Feeder reconfiguration scheme with integration of renewable energy sources using a Particle Swarm Optimisation method." Thesis, Cape Peninsula University of Technology, 2018. http://hdl.handle.net/20.500.11838/2712.
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Thesis (Master of Engineering in Electrical Engineering)--Cape Peninsula University of Technology, 2018.<br>A smart grid is an intelligent power delivery system integrating traditional and advanced control, monitoring, and protection systems for enhanced reliability, improved efficiency, and quality of supply. To achieve a smart grid, technical challenges such as voltage instability; power loss; and unscheduled power interruptions should be mitigated. Therefore, future smart grids will require intelligent solutions at transmission and distribution levels, and optimal placement & sizing of grid components for optimal steady state and dynamic operation of the power systems. At distribution levels, feeder reconfiguration and Distributed Generation (DG) can be used to improve the distribution network performance. Feeder reconfiguration consists of readjusting the topology of the primary distribution network by remote control of the tie and sectionalizing switches under normal and abnormal conditions. Its main applications include
service restoration after a power outage, load balancing by relieving overloads from some feeders to adjacent feeders, and power loss minimisation for better efficiency. On the other hand, the DG placement problem entails finding the optimal location and size of the DG for integration in a distribution network to boost the network performance. This research aims to develop Particle Swarm Optimization (PSO) algorithms to solve the distribution network feeder reconfiguration and DG placement & sizing problems. Initially, the feeder reconfiguration problem is treated as a single-objective optimisation problem (real power loss minimisation) and then converted into a multi-objective optimisation problem (real power loss minimisation and load balancing). Similarly, the DG placement problem is treated as a single-objective
problem (real power loss minimisation) and then converted into a multi-objective optimisation problem (real power loss minimisation, voltage deviation minimisation, Voltage stability Index maximisation). The developed PSO algorithms are implemented and tested for the 16-bus, the 33-bus, and the 69-bus IEEE distribution systems. Additionally, a parallel computing method is developed to study the operation of a distribution network with a feeder reconfiguration scheme under dynamic loading conditions.
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Grobler, Jacomine. "Particle swarm optimization and differential evolution for multi-objective multiple machine scheduling." Diss., Pretoria : [s.n.], 2009. http://upetd.up.ac.za/thesis/available/etd-05062009-164124/.
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Rastgoufard, Samin. "Applications of Artificial Intelligence in Power Systems." ScholarWorks@UNO, 2018. https://scholarworks.uno.edu/td/2487.
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Artificial intelligence tools, which are fast, robust and adaptive can overcome the drawbacks of traditional solutions for several power systems problems. In this work, applications of AI techniques have been studied for solving two important problems in power systems.
The first problem is static security evaluation (SSE). The objective of SSE is to identify the contingencies in planning and operations of power systems. Numerical conventional solutions are time-consuming, computationally expensive, and are not suitable for online applications. SSE may be considered as a binary-classification, multi-classification or regression problem. In this work, multi-support vector machine is combined with several evolutionary computation algorithms, including particle swarm optimization (PSO), differential evolution, Ant colony optimization for the continuous domain, and harmony search techniques to solve the SSE. Moreover, support vector regression is combined with modified PSO with a proposed modification on the inertia weight in order to solve the SSE.
Also, the correct accuracy of classification, the speed of training, and the final cost of using power equipment heavily depend on the selected input features. In this dissertation, multi-object PSO has been used to solve this problem. Furthermore, a multi-classifier voting scheme is proposed to get the final test output. The classifiers participating in the voting scheme include multi-SVM with different types of kernels and random forests with an adaptive number of trees. In short, the development and performance of different machine learning tools combined with evolutionary computation techniques have been studied to solve the online SSE. The performance of the proposed techniques is tested on several benchmark systems, namely the IEEE 9-bus, 14-bus, 39-bus, 57-bus, 118-bus, and 300-bus power systems.
The second problem is the non-convex, nonlinear, and non-differentiable economic dispatch (ED) problem. The purpose of solving the ED is to improve the cost-effectiveness of power generation. To solve ED with multi-fuel options, prohibited operating zones, valve point effect, and transmission line losses, genetic algorithm (GA) variant-based methods, such as breeder GA, fast navigating GA, twin removal GA, kite GA, and United GA are used. The IEEE systems with 6-units, 10-units, and 15-units are used to study the efficiency of the algorithms.
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Villeneuve, Frédéric. "A Method for Concept and Technology Exploration of Aerospace Architectures." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16212.
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This dissertation presents the development of a new concept and technology exploration methodology for aerospace architectures. The methodology is based on modeling the design space by a graph, and optimizing the graph using Ant Colony Optimization. The results show that the proposed design methodology can explore more efficiently the concept and technology space of a launch vehicle architecture than traditional optimization approaches such as Genetic Algorithm and Simulated Annealing.
The purpose of the method is to introduce quantitative and simultaneous exploration of concept and technology alternatives during the early phases of conceptual design. To achieve this goal, technical challenges such as expanding the size of the design space, exploring more efficiently the design options, and simultaneously considering technologies and concepts are overcome.
The total number of design alternatives grows factorially with the number of concepts in the design space. Under these circumstances, the design space is difficult to explore in its totality. Considering more alternatives has been the focus of several researchers, using Genetic Algorithms and Simulated Annealing. The large number of incompatibilities between alternatives, however, limits these optimization algorithms and reduces the number of concepts or technologies that can be considered.
To address these problems, a concept and technology selection methodology is developed. The methodology proposes a way to automatically generate aerospace architectures, and to model concept and technology incompatibilities by means of a graph. In conjunction with this new modeling approach, a graph-based stochastic optimization algorithm is used to efficiently explore the design space. This design methodology is applied to the simultaneous concept and technology exploration of an expendable launch vehicle architecture.
This study demonstrates that the consideration of more design alternatives can help design engineers to make more informed decisions during the concept and technology selection process. Moreover, the simultaneous exploration of concepts and technologies has the potential to identify a different set of solutions than the standard approach where the technologies are explored after the concepts have initially been selected.
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Vitanza, Alessandra. "Methodologies and Tools for the Emergence of Cooperation in Biorobotics." Doctoral thesis, Università di Catania, 2013. http://hdl.handle.net/10761/1310.
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One of the main purposes of the Ph.D. activities was the investigation of the swarm aspects
in order to formulate new strategies for the emergence of cooperation within a colony of robots.
The proposed idea was to furnish each robot with identical cognitive architectures,
applying them in a reward-based learning mechanism leading to the emergence of
cooperation capabilities and perform tasks where one robot alone cannot succeed.
The selected technique to induce the emergence of cooperation was based on a
selection of specific sub-set among the available behaviours.
During the learning phase, each robot can evolve its knowledge in order to specialize
its behaviours to acquire a specific role in the environment challenge.
The aim is to demonstrate how a group of robots, each one equipped
with an independent control system, thanks to the flexibility of the architecture can
learn cooperative behaviours through the specialization and organization of several
activities and roles to reach a common intent.
To permit the investigation of these new cooperation methodologies, one of the important
aims was the development of new tools and platform for multi-robot applications.
The thesis reports the methodology, the implemented tools and interesting applications which
underline the potentialities of this emergent approach to cooperative biorobotics.
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NOBILE, MARCO SALVATORE. "Evolutionary Inference of Biological Systems Accelerated on Graphics Processing Units." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/75434.
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In silico analysis of biological systems represents a valuable alternative and complementary approach to experimental research. Computational methodologies, indeed, allow to mimic some conditions of cellular processes that might be difficult to dissect by exploiting traditional laboratory techniques, therefore potentially achieving a thorough comprehension of the molecular mechanisms that rule the functioning of cells and organisms. In spite of the benefits that it can bring about in biology, the computational approach still has two main limitations: first, there is often a lack of adequate knowledge on the biological system of interest, which prevents the creation of a proper mathematical model able to produce faithful and quantitative predictions; second, the analysis of the model can require a massive number of simulations and calculations, which are computationally burdensome. The goal of the present thesis is to develop novel computational methodologies to efficiently tackle these two issues, at multiple scales of biological complexity (from single molecular structures to networks of biochemical reactions). The inference of the missing data — related to the three-dimensional structures of proteins, the number and type of chemical species and their mutual interactions, the kinetic parameters — is performed by means of novel methods based on Evolutionary Computation and Swarm Intelligence techniques. General purpose GPU computing has been adopted to reduce the computational time, achieving a relevant speedup with respect to the sequential execution of the same algorithms. The results presented in this thesis show that these novel evolutionary-based and GPU-accelerated methodologies are indeed feasible and advantageous from both the points of view of inference quality and computational performances.
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Vaddhireddy, Jyothirmye. "A Novel Swarm Intelligence based IWD Algorithm for Routing in MANETs." University of Toledo / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1321589580.
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PACÍFICO, Luciano Demétrio Santos. "Algoritmos de agrupamento particionais baseados na Meta-heurística de otimização por busca em grupo." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/18002.
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Previous issue date: 2016-08-26<br>CNPQ<br>A Análise de Agrupamentos, também conhecida por Aprendizagem Não-Supervisionada,
é uma técnica importante para a análise exploratória de dados, tendo sido largamente
empregada em diversas aplicações, tais como mineração de dados, segmentação de imagens,
bioinformática, dentre outras. A análise de agrupamentos visa a distribuição de um
conjunto de dados em grupos, de modo que indivíduos em um mesmo grupo estejam mais
proximamente relacionados (mais similares) entre si, enquanto indivíduos pertencentes a
grupos diferentes tenham um alto grau de dissimilaridade entre si.
Do ponto de vista de otimização, a análise de agrupamentos é considerada como um caso
particular de problema de NP-Difícil, pertencendo à categoria da otimização combinatória.
Técnicas tradicionais de agrupamento (como o algoritmo K-Means) podem sofrer algumas
limitações na realização da tarefa de agrupamento, como a sensibilidade à inicialização
do algoritmo, ou ainda a falta de mecanismos que auxiliem tais métodos a escaparem de
pontos ótimos locais.
Meta-heurísticas como Algoritmos Evolucionários (EAs) e métodos de Inteligência de
Enxames (SI) são técnicas de busca global inspirados na natureza que têm tido crescente
aplicação na solução de uma grande variedade de problemas difíceis, dada a capacidade de
tais métodos em executar buscas minuciosas pelo espaço do problema, tentando evitar
pontos de ótimos locais. Nas últimas décadas, EAs e SI têm sido aplicadas com sucesso
ao problema de agrupamento de dados. Nesse contexto, a meta-heurística conhecida por
Otimização por Busca em Grupo (GSO) vem sendo aplicada com sucesso na solução de
problemas difíceis de otimização, obtendo desempenhos superiores a técnicas evolucionárias
tradicionais, como os Algoritmos Genéticos (GA) e a Otimização por Enxame de Partículas
(PSO). No contexto de análise de agrupamentos, EAs e SIs são capazes de oferecer boas
soluções globais ao problema, porém, por sua natureza estocástica, essas abordagens
podem ter taxas de convergência mais lentas quando comparadas a outros métodos de
agrupamento.
Nesta tese, o GSO é adaptado ao contexto de análise de agrupamentos particional. Modelos
híbridos entre o GSO e o K-Means são apresentados, de modo a agregar o potencial de
exploração oferecido pelas buscas globais do GSO à velocidade de exploitação de regiões
locais oferecida pelo K-Means, fazendo com que os sistemas híbridos formados sejam
capazes de oferecerem boas soluções aos problemas de agrupamento tratados.
O trabalho apresenta um estudo da influência do K-Means quando usado como operador
de busca local para a inicialização populacional do GSO, assim como operador para
refinamento da melhor solução encontrada pela população do GSO durante o processo
geracional desenvolvido por esta técnica.
Uma versão cooperativa coevolucionária do modelo GSO também foi adaptada ao contexto
da análise de agrupamentos particional, resultando em um método com grande potencial
para o paralelismo, assim como para uso em aplicações de agrupamentos distribuídos.
Os resultados experimentais, realizados tanto com bases de dados reais, quanto com o
uso de conjuntos de dados sintéticos, apontam o potencial dos modelos alternativos de
inicialização da população propostos para o GSO, assim como de sua versão cooperativa
coevolucionária, ao lidar com problemas tradicionais de agrupamento de dados, como a
sobreposição entre as classes do problema, classes desbalanceadas, dentre outros, quando
em comparação com métodos de agrupamento existentes na literatura.<br>Cluster analysis, also known as unsupervised learning, is an important technique for
exploratory data analysis, and it has being widely employed in many applications such as
data mining, image segmentation, bioinformatics, and so on. Clustering aims to distribute
a data set in groups, in such a way that individuals from the same group are more closely
related (more similar) among each other, while individuals from different groups have a
high degree of dissimilarity among each other.
From an optimization perspective, clustering is considered as a particular kind of NP-hard
problem, belonging in the combinatorial optimization category. Traditional clustering
techniques (like K-Means algorithm) may suffer some limitations when dealing with
clustering task, such as the sensibility to the algorithm initialization, or the lack of
mechanisms to help these methods to escape from local minima points.
Meta-heuristics such as EAs and SI methods are nature-inspired global search techniques
which have been increasingly applied to solve a great variety of difficult problems, given
their capability to perform thorough searches through a problem space, attempting to
avoid local optimum points. From the past few decades, EAs and SI approaches have
been successfully applied to tackle clustering problems. In this context, Group Search
Optimization (GSO) meta-heuristic has been successfully applied to solve hard optimization
problems, obtaining better performances than traditional evolutionary techniques, such as
Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). In clustering context,
EAs an SIs are able to obtain good global solutions to the problem at hand, however,
according to their stochastic nature, these approaches may have slow convergence rates in
comparison to other clustering methods.
In this thesis, GSO is adapted to the context of partitional clustering analysis. Hybrid
models of GSO and K-Means are presented, in such a way that the exploration offered
by GSO global searches are combined with fast exploitation of local regions provided
by K-Means, generating new hybrid systems capable of obtaining good solutions to the
clustering problems at hands.
The work also presents a study on the influence of K-Means when adopted as a local
search operator for GSO population initialization, just like its application as an refinement
operator for the best solution found by GSO population during GSO generative process.
A cooperative coevolutionary variant of GSO model is adapted to the context of partitional
clustering, resulting in a method with great potential to parallelism, as much as for the
use in distributed clustering applications.
Experimental results, performed as with the use of real data sets, as with the use of
synthetic data sets, showed the potential of proposed alternative population initialization
models and the potential of GSO cooperative coevolutionary variant when dealing with
classic clustering problems, such as data overlapping, data unbalancing, and so on, in
comparison to other clustering algorithms from literature.
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Pierobom, Jean Lima. "Otimização por nuvem de partículas aplicada ao problema de atribuição de tarefas dinâmico." Universidade Tecnológica Federal do Paraná, 2012. http://repositorio.utfpr.edu.br/jspui/handle/1/205.
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A Inteligência de Enxame (Swarm Intelligence) é uma área de estudos que busca soluções para problemas de otimização utilizando-se de técnicas computacionais inspiradas no comportamento social emergente encontrado na biologia. A metaheurística Particle Swarm Optimization (PSO) é relativamente nova e foi inspirada no comportamento social de bandos de pássaros. PSO tem apresentado bons resultados em alguns trabalhos recentes de otimização discreta, apesar de ter sido concebido originalmente para a otimização de problemas contínuos. Este trabalho trata o Problema de Atribuição de Tarefas - Task Assignment Problem (TAP), e apresenta uma aplicação: o problema de alocação de táxis e clientes, cujo objetivo da otimização está em minimizar a distância percorrida pela frota. Primeiramente, o problema é resolvido em um cenário estático, com duas versões do PSO discreto: a primeira abordagem é baseada em codificação binária e a segunda utiliza permutações para codificar as soluções. Os resultados obtidos mostram que a segunda abordagem é superior à primeira em termos de qualidade das soluções e tempo computacional, e é capaz de encontrar as soluções ótimas para o problema nas instâncias para as quais os valores ótimos são conhecidos. A partir disto, o algoritmo é adaptado para a otimização do problema em um ambiente dinâmico, com a aplicação de diferentes estratégias de resposta às mudanças. Os novos resultados mostram que a combinação de algumas abordagens habilita o algoritmo PSO a obter boas soluções ao longo da ocorrência de mudanças nas variáveis de decisão problema, em todas as instâncias testadas, com diferentes tamanhos e escalas de mudança.<br>Swarm Intelligence searches for solutions to optimization problems using computational techniques inspired in the emerging social behavior found in biology. The metaheuristic Particle Swarm Optimization (PSO) is relatively new and can be considered a metaphor of bird flocks. PSO has shown good results in some recent works of discrete optimization, despite it has been originally designed for continuous optimization problems. This paper deals with the Task Assignment Problem (TAP), and presents an application: the optimization problem of allocation of taxis and customers, whose goal is to minimize the distance traveled by the fleet. The problem is solved in a static scenario with two versions of the discrete PSO: the first approach that is based on a binary codification and the second one which uses permutations to encode the solution. The obtained results show that the second approach is superior than the first one in terms of quality of the solutions and computational time, and it is capable of achieving the known optimal values in the tested instances of the problem. From this, the algorithm is adapted for the optimization of the problem in a dynamic environment, with the application of different strategies to respond to changes. The new results show that some combination of approaches enables the PSO algorithm to achieve good solutions along the occurrence of changes in decision variables problem, in all instances tested, with different sizes and scales of change.
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Wilke, Daniel N. "Analysis of the particle swarm optimization algorithm." Pretoria : [s.n.], 2005. http://upetd.up.ac.za/thesis/available/etd-01312006-125743.
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Alkindy, Bassam. "Combining approaches for predicting genomic evolution." Thesis, Besançon, 2015. http://www.theses.fr/2015BESA2012/document.
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En bio-informatique, comprendre comment les molécules d’ADN ont évolué au cours du temps reste un problème ouvert etcomplexe. Des algorithmes ont été proposés pour résoudre ce problème, mais ils se limitent soit à l’évolution d’un caractèredonné (par exemple, un nucléotide précis), ou se focalisent a contrario sur de gros génomes nucléaires (plusieurs milliardsde paires de base), ces derniers ayant connus de multiples événements de recombinaison – le problème étant NP completquand on considère l’ensemble de toutes les opérations possibles sur ces séquences, aucune solution n’existe à l’heureactuelle. Dans cette thèse, nous nous attaquons au problème de reconstruction des séquences ADN ancestrales en nousfocalisant sur des chaînes nucléotidiques de taille intermédiaire, et ayant connu assez peu de recombinaison au coursdu temps : les génomes de chloroplastes. Nous montrons qu’à cette échelle le problème de la reconstruction d’ancêtrespeut être résolu, même quand on considère l’ensemble de tous les génomes chloroplastiques complets actuellementdisponibles. Nous nous concentrons plus précisément sur l’ordre et le contenu ancestral en gènes, ainsi que sur lesproblèmes techniques que cette reconstruction soulève dans le cas des chloroplastes. Nous montrons comment obtenirune prédiction des séquences codantes d’une qualité telle qu’elle permette ladite reconstruction, puis comment obtenir unarbre phylogénétique en accord avec le plus grand nombre possible de gènes, sur lesquels nous pouvons ensuite appuyernotre remontée dans le temps – cette dernière étant en cours de finalisation. Ces méthodes, combinant l’utilisation d’outilsdéjà disponibles (dont la qualité a été évaluée) à du calcul haute performance, de l’intelligence artificielle et de la biostatistique,ont été appliquées à une collection de plus de 450 génomes chloroplastiques<br>In Bioinformatics, understanding how DNA molecules have evolved over time remains an open and complex problem.Algorithms have been proposed to solve this problem, but they are limited either to the evolution of a given character (forexample, a specific nucleotide), or conversely focus on large nuclear genomes (several billion base pairs ), the latter havingknown multiple recombination events - the problem is NP complete when you consider the set of all possible operationson these sequences, no solution exists at present. In this thesis, we tackle the problem of reconstruction of ancestral DNAsequences by focusing on the nucleotide chains of intermediate size, and have experienced relatively little recombinationover time: chloroplast genomes. We show that at this level the problem of the reconstruction of ancestors can be resolved,even when you consider the set of all complete chloroplast genomes currently available. We focus specifically on the orderand ancestral gene content, as well as the technical problems this raises reconstruction in the case of chloroplasts. Weshow how to obtain a prediction of the coding sequences of a quality such as to allow said reconstruction and how toobtain a phylogenetic tree in agreement with the largest number of genes, on which we can then support our back in time- the latter being finalized. These methods, combining the use of tools already available (the quality of which has beenassessed) in high performance computing, artificial intelligence and bio-statistics were applied to a collection of more than450 chloroplast genomes
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Maripi, Jagadish Kumar. "AN EFFECTIVE PARALLEL PARTICLE SWARM OPTIMIZATION ALGORITHM AND ITS PERFORMANCE EVALUATION." OpenSIUC, 2010. https://opensiuc.lib.siu.edu/theses/275.
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Population-based global optimization algorithms including Particle Swarm Optimization (PSO) have become popular for solving multi-optima problems much more efficiently than the traditional mathematical techniques. In this research, we present and evaluate a new parallel PSO algorithm that provides a significant performance improvement as compared to the serial PSO algorithm. Instead of merely assigning parts of the task of serial version to several processors, the new algorithm places multiple swarms on the available nodes in which operate independently, while collaborating on the same task. With the reduction of the communication bottleneck as well the ability to manipulate the individual swarms independently, the proposed approach outperforms the original PSO algorithm and still maintains the simplicity and ease of implementation.
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Li, Futong. "Global Optimization Techniques Based on Swarm-intelligent and Gradient-free Algorithms." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42307.
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The need for solving nonlinear optimization problems is pervasive in many fields. Particle swarm optimization, advantageous with the simple underlying implementation logic, and simultaneous perturbation stochastic approximation, which is famous for its saving in the computational power with the gradient-free attribute, are two solutions that deserve attention. Many researchers have exploited their merits in widely challenging applications. However, there is a known fact that both of them suffer from a severe drawback, non- effectively converging to the global best solution, because of the local “traps” spreading on the searching space. In this article, we propose two approaches to remedy this issue by combined their advantages.
In the first algorithm, the gradient information helps optimize half of the particles at the initialization stage and then further updates the global best position. If the global best position is located in one of the local optima, the searching surface’s additional gradient estimation can help it jump out. The second algorithm expands the implementation of the gradient information to all the particles in the swarm to obtain the optimized personal best position. Both have to obey the rule created for updating the particle(s); that is, the solution found after employing the gradient information to the particle(s) has to perform more optimally.
In this work, the experiments include five cases. The three previous methods with a similar theoretical basis and the two basic algorithms take participants in all five. The experimental results prove that the proposed two algorithms effectively improved the basic algorithms and even outperformed the previously designed three algorithms in some scenarios.
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Albin, Aaron Thomas. "Musical swarm robot simulation strategies." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42862.
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Swarm robotics for music is a relatively new way to explore algorithmic composition as well as new modes of human robot interaction. This work outlines a strategy for making music with a robotic swarm constrained by acoustic sound, rhythmic music using sequencers, motion causing changes in the music, and finally human and swarm interaction. Two novel simulation programs are created in this thesis: the first is a multi-agent simulation designed to explore suitable parameters for motion to music mappings as well as parameters for real time interaction. The second is a boid-based robotic swarm simulation that adheres to the constraints established, using derived parameters from the multi-agent simulation: orientation, number of neighbors, and speed. In addition, five interaction modes are created that vary along an axis of direct and indirect forms of human control over the swarm motion. The mappings and interaction modes of the swarm robot simulation are evaluated in a user study involving music technology students. The purpose of the study is to determine the legibility of the motion to musical mappings and evaluate user preferences for the mappings and modes of interaction in problem solving and in open-ended contexts. The findings suggest that typical users of a swarm robot system do not necessarily prefer more inherently legible mappings in open-ended contexts. Users prefer direct and intermediate modes of interaction in problem solving scenarios, but favor intermediate modes of interaction in open-ended ones. The results from this study will be used in the design and development of a new swarm robotic system for music that can be used in both contexts.
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Harrison, Kyle Robert. "An Analysis of Parameter Control Mechanisms for the Particle Swarm Optimization Algorithm." Thesis, University of Pretoria, 2018. http://hdl.handle.net/2263/66103.
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The particle swarm optimization (PSO) algorithm is a stochastic, population-based optimization technique influenced by social dynamics. It has been shown that the performance of the PSO algorithm can be greatly improved if the control parameters are appropriately tuned. However, the tuning of control parameter values has traditionally been a time-consuming, empirical process followed by statistical analysis. Furthermore, ideal values for the control parameters may be time-dependent; parameter values that lead to good performance in an exploratory phase may not be ideal for an exploitative phase. Self-adaptive algorithms eliminate the need to tune parameters in advance, while also providing real-time behaviour adaptation based on the current problem.
This thesis first provides an in-depth review of existing self-adaptive particle swarm optimization (SAPSO) techniques. Their ability to attain order-2 stability is examined and it is shown that a majority of the existing SAPSO algorithms are guaranteed to exhibit either premature convergence or rapid divergence. A further investigation focusing on inertia weight control strategies demonstrates that none of the examined techniques outperform a static value. This thesis then investigates the performance of a wide variety of PSO parameter configurations, thereby discovering regions in parameter space that lead to good performance. This investigation provides strong empirical evidence that the best values to employ for the PSO control parameters change over time. Finally, this thesis proposes novel PSO variants inspired by results of the aforementioned studies.<br>Thesis (PhD)--University of Pretoria, 2018.<br>Computer Science<br>PhD<br>Unrestricted
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Masmoudi, Nesrine. "Modèle bio-inspiré pour le clustering de graphes : application à la fouille de données et à la distribution de simulations." Thesis, Normandie, 2017. http://www.theses.fr/2017NORMLH26/document.
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Dans ce travail de thèse, nous présentons une méthode originale s’inspirant des comportements des fourmis réelles pour la résolution de problème de classification non supervisée non hiérarchique. Cette approche créée dynamiquement des groupes de données. Elle est basée sur le concept des fourmis artificielles qui se déplacent en même temps de manière complexe avec les règles de localisation simples. Chaque fourmi représente une donnée dans l’algorithme. Les mouvements des fourmis visent à créer des groupes homogènes de données qui évoluent ensemble dans une structure de graphe. Nous proposons également une méthode de construction incrémentale de graphes de voisinage par des fourmis artificielles. Nous proposons deux méthodes qui se dérivent parmi les algorithmes biomimétiques. Ces méthodes sont hybrides dans le sens où la recherche du nombre de classes, de départ, est effectuée par l’algorithme de classification K-Means, qui est utilisé pour initialiser la première partition et la structure de graphe<br>In this work, we present a novel method based on behavior of real ants for solving unsupervised non-hierarchical classification problem. This approach dynamically creates data groups. It is based on the concept of artificial ants moving complexly at the same time with simple location rules. Each ant represents a data in the algorithm. The movements of ants aim to create homogenous data groups that evolve together in a graph structure. We also propose a method of incremental building neighborhood graphs by artificial ants. We propose two approaches that are derived among biomimetic algorithms, they are hybrid in the sense that the search for the number of classes starting, which are performed by the classical algorithm K-Means classification, it is used to initialize the first partition and the graph structure
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Amlie-Wolf, Alexandre. "A Swarm of Salesman: Algorithmic Approaches to Multiagent Modeling." Oberlin College Honors Theses / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin1368052652.
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Reghelin, Ricardo. "Um modelo de gerenciamento microscópico centralizado de tráfego de veículos inteligentes em um segmento de rodovia." Universidade Tecnológica Federal do Paraná, 2014. http://repositorio.utfpr.edu.br/jspui/handle/1/953.
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Este trabalho insere-se na área de pesquisa de sistemas de transporte inteligente e mobilidade urbana buscando um cenário onde a infraestrutura rodoviária é capaz de monitorar um tráfego exclusivo de veículos inteligentes que não dependem de motoristas para serem guiados. A principal contribuição do trabalho é o desenvolvimento de uma solução matemática para otimizar o gerenciamento microscópico centralizado do tráfego de veículos inteligentes em trechos (segmentos) de rodovia. Para isto é apresentado um modelo de otimização baseado em Programação Linear Inteira Mista (MILP), que determina um plano ótimo de trajetórias individuais dos veículos em uma evolução de tráfego. O objetivo é reduzir o tempo de viagem individualmente e assegurar fluidez do tráfego. O modelo considera componentes essenciais do sistema dinâmico viário como topografia da pista, regras de trânsito e a curva de aceleração máxima de cada veículo. São contempladas várias situações de tráfego, tais como ultrapassagens, inclinação na pista, obstáculos e redutores de velocidade. Os resultados indicaram uma média de 20,5 segundos para o cálculo de um cenário com 6 veículos e 11 intervalos de tempo. Como o modelo MILP não tem solução em tempo computacional aceitável para aplicação real, também é proposto um algoritmo de simulação baseado em heurísticas o qual busca reduzir esse tempo de cálculo em detrimento da otimalidade da solução. O algoritmo reproduz o comportamento de um motorista que tenta manter sempre um valor de velocidade escolhido previamente, e por isso é forçado a ultrapassar outros veículos quando obstruído ao longo do trajeto. O resultado do algoritmo tem importância adicional, pois serve de referência para resolver o problema da prioridade nas ultrapassagens. Também são propostos novos indicadores para a avaliação microscópica de qualidade de tráfego. Finalmente, são apresentados resultados de testes em simulações a fim de avaliar e validar o modelo e o algoritmo.<br>This work focus on the research area of intelligent transportation systems and urban mobility. It considers a scenario where the roadside infrastructure is capable of monitoring traffic composed by 100% of intelligent vehicles that do not rely on drivers to be guided. The main contribution of this work is the development of a mathematical solution to optimize the centralized management of intelligent microscopic vehicular traffic in parts (segments) of highway. Therefore an optimization model based on Mixed Integer Linear Programming (MILP) is presented. The model determines individual trajectories plans of vehicles in a traffic evolution. The objective is to reduce the travel time individually and ensure traffic flow. The model considers essential components of the dynamic highway system, such as, topography of the lane, traffic rules and acceleration curve for each vehicle. Many traffic situations are considered, such as, overtaking, slopes, obstacles and speed reducers. The results indicated an average of 20.5 seconds to calculate a scenario with 6 vehicles and 11 time intervals. As the MILP model has no solution in acceptable computational time for real application, it is proposed an algorithm based on heuristic simulation which seeks to reduce the computation time at the expense of optimality of the solution. The algorithm reproduces the behavior of a driver who always tries to maintain a preselected velocity value, and is therefore forced to overtake other vehicles when blocked along the path. The result of the algorithm has additional importance because it serves as a reference for solving the problem of priority when overtaking. New indicators for microscopic evaluation of quality traffic are also proposed. Finally, test results are presented on simulations to evaluate and validate the model and algorithm.
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Tai, Hio Kuan. "Protein-ligand docking and virtual screening based on chaos-embedded particle swarm optimization algorithm." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3948431.
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Palangpour, Parviz Michael. "FFGA implementation of PSO algorithm and neural networks." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2010. http://scholarsmine.mst.edu/thesis/pdf/Palangpour_09007dcc8078a58e.pdf.
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Thesis (M.S.)--Missouri University of Science and Technology, 2010.<br>Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed April 8, 2010) Includes bibliographical references (p. 76-78).
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Usman, Modibo. "The Effect of the Implementation of a Swarm Intelligence Algorithm on the Efficiency of the Cosmos Open Source Managed Operating System." Thesis, Northcentral University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10810882.
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<p> As the complexity of mankind’s day-to-day challenges increase, so does a need for the optimization of know solutions to accommodate for this increase in complexity. Today’s computer systems use the Input, Processing, and Output (IPO) model as a way to deliver efficiency and optimization in human activities. Since the relative quality of an output utility derived from an IPO based computer system is closely coupled to the quality of its input media, the measure of the Optimal Quotient (OQ) is the ratio of the input to output which is 1:1. This relationship ensures that all IPO based computers are not just linearly predictable, but also characterized by the Garbage In Garbage Out (GIGO) design concept. While current IPO based computer systems have been relatively successful at delivering some measure of optimization, there is a need to examine (Li & Malik, 2016) alternative methods of achieving optimization. The purpose of this quantitative research study, through an experimental research design, is to determine the effects of the application of a Swarm Intelligence algorithm on the efficiency of the Cosmos Open Source Managed Operating System. </p><p> By incorporating swarm intelligence into an improved IPO design, this research addresses the need for optimization in computer systems through the creation of an improved operating system Scheduler. The design of a Swarm Intelligence Operating System (SIOS) is an attempt to solve some inherent vulnerabilities and problems of complexity and optimization otherwise unresolved in the design of conventional operating systems. This research will use the Cosmos open source operating system as a test harness to ensure improved internal validity while the subsequent measurement between the conventional and improved IPO designs will demonstrate external validity to real world applications. </p><p>
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Liu, Zhiyong. "Ant Based Algorithm and Robustness Metric in Spare Capacity Allocation for Survivable Routing." Thesis, University of Canterbury. Electrical and Computer Engineering, 2010. http://hdl.handle.net/10092/4099.
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Network resiliency pertains to the vulnerability of telecommunication networks in the case of failures and malicious attacks. With the increasing capacity catering of network for the booming multi-services in Next Generation Networks (NGNs), reducing recovery time and improving capacity efficiency while providing high quality and resiliency of services has become increasingly important for the future network development. Providing network resiliency means to rapidly and accurately reroute the traffic via diversely routed spare capacity in the network when a failure takes down links or nodes in the working path. Planning and optimization for NGNs require an efficient algorithm for spare capacity allocation (SCA) that assures restorability with a minimum of total capacity. This dissertation aims to understand and advance the state of knowledge on spare capacity allocation in network resiliency for telecommunication core networks.
Optimal network resiliency design for restorability requires considering: network topology, working and protection paths routing and spare capacity allocation. Restorable networks should be highly efficient in terms of total capacity required for restorability and be able to support any target level of restorability. The SCA strategy is to decide how much spare capacity should be reserved on links and to pre-plan protection paths to protect traffic from a set of failures. This optimal capacity allocation problem for survivable routing is known as NP-complete. To expose the problem structure, we propose a model of the SCA problem using a matrix-based framework, named Distributed Resilience Matrix (DRM) to identify the dependencies between the working and protection capacities associated with each pair of links and also to capture the local capacity usage information in a distributed control environment. In addition, we introduce a novel ant-based heuristic algorithm, called Friend-or-Foe Resilient (FoF-R) ant-based routing algorithm to find the optimal protection cycle (i.e., two node-disjoint paths between a source-destination node pair) and explore the sharing ability among protection paths using a capacity headroom-dependent attraction and repulsion function. Simulation results based on the OMNeT++ and AMPL/CPLEX tools show that the FoF-R scheme with the DRM structure is a promising approach to solving the SCA problem for survivable routing and it gives a good trade off between solution optimality and computation speed.
Furthermore, for the SCA studies of survivable networks, it is also important to be able to differentiate between network topologies by means of a robust numerical measure that indicates the level of immunity of these topologies to failures of their nodes and links. Ideally, such a measure should be sensitive to the existence of nodes or links, which are more important than others, for example, if their failure causes the network’s disintegration. Another contribution in this dissertation is to introduce an algebraic connectivity metric, adopted from the spectral graph theory, namely the 2nd smallest eigenvalue of the Laplacian matrix of the network topology, instead of the average nodal degree, to characterize network robustness in studies of the SCA problem. Extensive simulation studies confirm that this metric is a more informative parameter than the average nodal degree for characterizing network topologies in network resiliency studies.
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Al-Kanhal, Tawfeeq. "An intelligent manufacturing system for heat treatment scheduling." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/4495.
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This research is focused on the integration problem of process planning and scheduling in steel heat treatment operations environment using artificial intelligent techniques that are capable of dealing with such problems. This work addresses the issues involved in developing a suitable methodology for scheduling heat treatment operations of steel. Several intelligent algorithms have been developed for these propose namely, Genetic Algorithm (GA), Sexual Genetic Algorithm (SGA), Genetic Algorithm with Chromosome differentiation (GACD), Age Genetic Algorithm (AGA), and Mimetic Genetic Algorithm (MGA). These algorithms have been employed to develop an efficient intelligent algorithm using Algorithm Portfolio methodology. After that all the algorithms have been tested on two types of scheduling benchmarks. To apply these algorithms on heat treatment scheduling, a furnace model is developed for optimisation proposes. Furthermore, a system that is capable of selecting the optimal heat treatment regime is developed so the required metal properties can be achieved with the least energy consumption and the shortest time using Neuro-Fuzzy (NF) and Particle Swarm Optimisation (PSO) methodologies. Based on this system, PSO is used to optimise the heat treatment process by selecting different heat treatment conditions. The selected conditions are evaluated so the best selection can be identified. This work addresses the issues involved in developing a suitable methodology for developing an NF system and PSO for mechanical properties of the steel. Using the optimisers, furnace model and heat treatment system model, the intelligent system model is developed and implemented successfully. The results of this system were exciting and the optimisers were working correctly.
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Santos, Daniela Scherer dos. "Bee clustering : um algoritmo para agrupamento de dados inspirado em inteligência de enxames." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/18249.
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Agrupamento de dados é o processo que consiste em dividir um conjunto de dados em grupos de forma que dados semelhantes entre si permaneçam no mesmo grupo enquanto que dados dissimilares sejam alocados em grupos diferentes. Técnicas tradicionais de agrupamento de dados têm sido usualmente desenvolvidas de maneira centralizada dependendo assim de estruturas que devem ser acessadas e modificadas a cada passo do processo de agrupamento. Além disso, os resultados gerados por tais métodos são dependentes de informações que devem ser fornecidas a priori como por exemplo número de grupos, tamanho do grupo ou densidade mínima/máxima permitida para o grupo. O presente trabalho visa propor o bee clustering, um algoritmo distribuído inspirado principalmente em técnicas de inteligência de enxames como organização de colônias de abelhas e alocação de tarefas em insetos sociais, desenvolvido com o objetivo de resolver o problema de agrupamento de dados sem a necessidade de pistas sobre o resultado desejado ou inicialização de parâmetros complexos. O bee clustering é capaz de formar grupos de agentes de maneira distribuída, uma necessidade típica em cenários de sistemas multiagente que exijam capacidade de auto-organização sem controle centralizado. Os resultados obtidos mostram que é possível atingir resultados comparáveis as abordagens centralizadas.<br>Clustering can be defined as a set of techniques that separate a data set into groups of similar objects. Data items within the same group are more similar than objects of different groups. Traditional clustering methods have been usually developed in a centralized fashion. One reason for this is that this form of clustering relies on data structures that must be accessed and modified at each step of the clustering process. Another issue with classical clustering methods is that they need some hints about the target clustering. These hints include for example the number of clusters, the expected cluster size, or the minimum density of clusters. In this work we propose a clustering algorithm that is inspired by swarm intelligence techniques such as the organization of bee colonies and task allocation among social insects. Our proposed algorithm is developed in a decentralized fashion without any initial information about number of classes, number of partitions, and size of partition, and without the need of complex parameters. The bee clustering algorithm is able to form groups of agents in a distributed way, a typical necessity in multiagent scenarios that require self-organization without central control. The performance of our algorithm shows that it is possible to achieve results that are comparable to those from centralized approaches.
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Wong, Cheok Meng. "A distributed particle swarm optimization for fuzzy c-means algorithm based on an apache spark platform." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950604.
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Fernández, Pérez Iñaki. "Distributed Embodied Evolutionary Adaptation of Behaviors in Swarms of Robotic Agents." Electronic Thesis or Diss., Université de Lorraine, 2017. http://www.theses.fr/2017LORR0300.
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Les essaims de robots sont des systèmes composés d’un grand nombre de robots relativement simples. Du fait du grand nombre d’unités, ces systèmes ont de bonnes propriétés de robustesse et de passage à l’échelle. Néanmoins, il reste en général difficile de concevoir manuellement des contrôleurs pour les essaims de robots, à cause de la grande complexité des interactions inter-robot. Par conséquent, les approches automatisées pour l’apprentissage de comportements d’essaims de robots constituent une alternative attrayante. Dans cette thèse, nous étudions l’adaptation de comportements d’essaim de robots avec des méthodes de Embodied Evolutionary Robotics (EER) distribuée. Ainsi, nous fournissons trois contributions principales : (1) Nous étudions l’influence de la pression à la sélection dirigée vers une tâche dans un essaim d’agents robotiques qui utilisent une approche d’EER distribuée. Nous évaluons l’impact de différents opérateurs de sélection dans un algorithme d’EER distribuée pour un essaim de robots. Nos résultats montrent que le plus forte la pression à la sélection est, les meilleures performances sont atteintes lorsque les robots doivent s’adapter à des tâches particulières. (2) Nous étudions l’évolution de comportements collaboratifs pour une tâche de récolte d’objets dans un essaim d’agents robotiques qui utilisent une approche d’EER distribuée. Nous réalisons un ensemble d’expériences où un essaim de robots s’adapte à une tâche collaborative avec un algorithme d’EER distribuée. Nos résultats montrent que l’essaim s’adapte à résoudre la tâche, et nous identifions des limitations concernant le choix d’action. (3) Nous proposons et validons expérimentalement un mécanisme complètement distribué pour adapter la structure des neurocontrôleurs des robots dans un essaim qui utilise une approche d’EER distribuée, ce qui permettrait aux neurocontrôleurs d’augmenter leur expressivité. Nos expériences montrent que notre mécanisme, qui est complètement décentralisé, fournit des résultats similaires à un mécanisme qui dépend d’une information globale<br>Robot swarms are systems composed of a large number of rather simple robots. Due to the large number of units, these systems, have good properties concerning robustness and scalability, among others. However, it remains generally difficult to design controllers for such robotic systems, particularly due to the complexity of inter-robot interactions. Consequently, automatic approaches to synthesize behavior in robot swarms are a compelling alternative. In this thesis, we focus on online behavior adaptation in a swarm of robots using distributed Embodied Evolutionary Robotics (EER) methods. To this end, we provide three main contributions: (1) We investigate the influence of task-driven selection pressure in a swarm of robotic agents using a distributed EER approach. We evaluate the impact of a range of selection pressure strength on the performance of a distributed EER algorithm. The results show that the stronger the task-driven selection pressure, the better the performances obtained when addressing given tasks. (2) We investigate the evolution of collaborative behaviors in a swarm of robotic agents using a distributed EER approach. We perform a set of experiments for a swarm of robots to adapt to a collaborative item collection task that cannot be solved by a single robot. Our results show that the swarm learns to collaborate to solve the task using a distributed approach, and we identify some inefficiencies regarding learning to choose actions. (3) We propose and experimentally validate a completely distributed mechanism that allows to learn the structure and parameters of the robot neurocontrollers in a swarm using a distributed EER approach, which allows for the robot controllers to augment their expressivity. Our experiments show that our fully-decentralized mechanism leads to similar results as a mechanism that depends on global information
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Vignogna, Antoniangelo. "Swarm of Drones: il futuro delle tecnologie autonome." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20385/.
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Partendo dalla definizione di APR “aeromobile a pilotaggio remoto”, meglio conosciuto come DRONE, questo lavoro di tesi si propone di esaminare gli aspetti principali di tale tecnologia e di osservare come si è evoluta nel tempo, cominciando dalle origini fino ad arrivare alla nascita dei droni con intelligenza artificiale. Successivamente, viene posta l’attenzione sugli “Swarm of Drones”, droni autonomi capaci di volare in gruppo interagendo con l’ambiente circostante ma senza interferire tra di loro. Viene, poi, presentato il concetto di “Swarm Intelligence”, analizzando la sua precisione, affidabilità e vulnerabilità. A questo punto vengono esaminati i vari ambiti di utilizzo, partendo da quello civile e scientifico fino ad arrivare a quello militare e della sicurezza. A seguire, viene posta particolare attenzione sui futuri casi d’impiego che hanno già suscitato non poche polemiche e sugli aspetti etici che già dividono l’opinione pubblica, analizzano, infine, le possibili soluzioni per disciplinare e controllare tali tecnologie. Tra i risultati che questo lavoro di tesi ha portato alla luce spicca il fatto che opere di elevata tecnologia quali i droni emulino dei fattori biologici. Il loro sviluppo, infatti, ha avuto origine dallo studio delle dinamiche comportamentali degli insetti sociali. I ricercatori, tramite l’osservazione di questi fenomeni naturali sono riusciti a creare degli algoritmi per la risoluzione di problemi complessi che sono poi stati utilizzati per la creazione degli sciami di droni, spesso impiegati anche in azioni militari. Ovviamente, sono emersi anche i vantaggi e gli svantaggi di tali creazioni. Tra gli svantaggi emerge la costruzione e lo sviluppo delle armi autonome che fa porre l’attenzione sugli aspetti etici del loro utilizzo. Per quanto concerne i vantaggi, sicuramente l’utilizzo dei droni in campo medico potrà essere di vitale importanza per lo sviluppo di un innovativo sistema in grado di combattere le cellule tumorali.
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Pereira, Andre Luiz Vizine. "Busca na web e agrupamento de textos usando computação inspirada na biologia." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259821.
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Orientadores: Ricardo Ribeiro Gudwin, Leandro Nunes de Castro Silva<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação<br>Made available in DSpace on 2018-08-11T06:40:01Z (GMT). No. of bitstreams: 1
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Previous issue date: 2007<br>Resumo: A Internet tornou-se um dos principais meios de comunicação da atualidade, reduzindo custos, disponibilizando recursos e informação para pessoas das mais diversas áreas e interesses. Esta dissertação desenvolve e aplica duas abordagens de computação inspirada na biologia aos problemas de otimização do processo de busca e recuperação de informação na web e agrupamento de textos. Os algoritmos investigados e modificados são o algoritmo genético e o algoritmo de agrupamento por colônia de formigas. O objetivo final do trabalho é desenvolver parte do conjunto de ferramentas que será usado para compor o núcleo de uma comunidade virtual acadêmica adaptativa. Os resultados obtidos mostraram que o algoritmo genético é uma ferramenta adequada para otimizar a busca de informação na web, mas o algoritmo de agrupamento por colônia de formigas ainda apresenta limitações quanto a sua aplicabilidade para agrupamento de textos.<br>Abstract: The Internet became one of the main sources of information and means of communication, reducing costs and providing resources and information to the people all over the world. This dissertation develops and applies two biologically-inspired computing approaches, namely a genetic algorithm and the ant-clustering algorithm, to the problems of optimizing the information search and retrieval over the web, and to perform text clustering. The final goal of this project is to design and develop some of the tools to be used to construct an adaptive academic virtual community. The results obtained showed that the genetic algorithm can be feasibly applied to the optimizing information search and retrieval, whilest the ant-clustering algorithm needs further investigation in order to be efficiently applied to text clustering.<br>Mestrado<br>Engenharia de Computação<br>Mestre em Engenharia Elétrica
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Kroetz, Marcel Giovani. "Sistema de apoio na inspeção radiográfica computadorizada de juntas soldadas de tubulações de petróleo." Universidade Tecnológica Federal do Paraná, 2012. http://repositorio.utfpr.edu.br/jspui/handle/1/509.
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Petrobras<br>A inspeção radiográfica de juntas soldadas de tubulações é a atividade minuciosa e cuidadosa de observar imagens radiográficas de juntas soldadas em busca de pequenos defeitos e descontinuidades que possam comprometer a resistência mecânica dessas juntas. Como toda atividade que requer atenção constante, a inspeção radiográfica está sujeita a erros principalmente devido a fadiga visual e distrações naturais devido a repetitividade e monotonia inerentes à essa atividade. No presente trabalho, apresentam-se duas metodologias que têm por objetivo o auxílio e a automação da atividade de inspeção: a detecção automática dos cordões de solda nas radiografias e o realce das descontinuidades; compondo entre outras funcionalidades, um aplicativo completo de auxílio na inspeção radiográfica que agrega ainda a possibilidade de automação do processamento dessas imagens através da construção de rotinas e sua posterior aplicação a um conjunto de imagens semelhantes. Os resultados obtidos na detecção automática do cordão de solda são promissores, sendo possível, através da metodologia proposta, detectar cordões provenientes diferentes técnicas de ensaios radiográficos usuais. Quanto aos resultados do realce das descontinuidades, apesar de estes ainda não levarem a uma inspeção completamente autônoma e não supervisionada, apresentam resultados melhores do que aqueles existentes atualmente na literatura, principalmente quanto a correlação entre contraste visual do resultado do realce e a probabilidade de ocorrência de descontinuidades nas regiões demarcadas. Por fim, o realce das descontinuidades em conjunto com um aplicativo completo e iterativo contribui para uma maior leveza na atividade de inspeção, com o que se espera uma expressiva redução das taxas de erro devido à fadiga visual e um aumento considerável da produtividade através da automação das rotinas mais repetitivas de processamento digital a que as imagens radiográficas são submetidas durante sua inspeção.<br>The weld bead radiographic inspection is the activity of meticulously observe a radiographic image looking for small defects and discontinuities in the welded joints that can compromise the mechanical resistance of that joints. As any other activity than requires constant attention, the weld bead inspection is error prone due to visual fatigue, repetition and others distractions inherent to these activity. In this work, two new methodologies for help in the inspection activities are presented: the automatic detection of the weld bead and the highlighting of the weld bead discontinuities. Those that, among others functionalities, are included in a complete software solution for help in the weld bead inspection. Including the feature of macro programing for automation of the most common image processing routines and further processing bath of images in an automatic way. The results from the automatic weld bead detection is beyond the satisfactory, detecting weld bead from all the usual radiographic techniques. About the results of the highlight of the discontinuities, although that are not suited for a complete non supervised weld bead inspection, their correlation among intensity and the probability of the presence of a discontinuity is very well suited for discontinuities highlighting, a helpful tool in weld bead inspection. In conclusion, the proposed methodologies. combined with a fully featured interactive software solution, a lot contribute for the weld bead inspection activity, a decreased error rate due to visual fatigue and a better overall performance due to the automation of the most common procedures involved in this activity.
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Mendonça, Rafael Mathias de. "Algoritmos distribuídos para alocação dinâmica de tarefas em enxame de robôs." Universidade do Estado do Rio de Janeiro, 2014. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=8140.
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A Inteligência de Enxame foi proposta a partir da observação do comportamento social de espécies de insetos, pássaros e peixes. A ideia central deste comportamento coletivo é executar uma tarefa complexa decompondo-a em tarefas simples, que são facilmente executadas pelos indivíduos do enxame. A realização coordenada destas tarefas simples, respeitando uma proporção pré-definida de execução, permite a realização da tarefa complexa. O problema de alocação de tarefas surge da necessidade de alocar as tarefas aos indivíduos de modo coordenado, permitindo o gerenciamento do enxame. A alocação de tarefas é um processo dinâmico pois precisa ser continuamente ajustado em resposta a alterações no ambiente, na configuração do enxame e/ou no desempenho do mesmo. A robótica de enxame surge deste contexto de cooperação coletiva, ampliada à robôs reais. Nesta abordagem, problemas complexos são resolvidos pela realização de tarefas complexas por enxames de robôs simples, com capacidade de processamento e comunicação limitada. Objetivando obter flexibilidade e confiabilidade, a alocação deve emergir como resultado de um processo distribuído. Com a descentralização do problema e o aumento do número de robôs no enxame, o processo de alocação adquire uma elevada complexidade. Desta forma, o problema de alocação de tarefas pode ser caracterizado como um processo de otimização que aloca as tarefas aos robôs, de modo que a proporção desejada seja atendida no momento em que o processo de otimização encontre a solução desejada. Nesta dissertação, são propostos dois algoritmos que seguem abordagens distintas ao problema de alocação dinâmica de tarefas, sendo uma local e a outra global. O algoritmo para alocação dinâmica de tarefas com abordagem local (ADTL) atualiza a alocação de tarefa de cada robô a partir de uma avaliação determinística do conhecimento atual que este possui sobre as tarefas alocadas aos demais robôs do enxame. O algoritmo para alocação dinâmica de tarefas com abordagem global (ADTG) atualiza a alocação de tarefas do enxame com base no algoritmo de otimização PSO (Particle swarm optimization). No ADTG, cada robô possui uma possível solução para a alocação do enxame que é continuamente atualizada através da troca de informação entre os robôs. As alocações são avaliadas quanto a sua aptidão em atender à proporção-objetivo. Quando é identificada a alocação de maior aptidão no enxame, todos os robôs do enxame são alocados para as tarefas definidas por esta alocação. Os algoritmos propostos foram implementados em enxames com diferentes arranjos de robôs reais demonstrando sua eficiência e eficácia, atestados pelos resultados obtidos.<br>Swarm Intelligence has been proposed based on the observation of social behavior of insect species, birds and fishes. The main idea of this collective behavior is to perform a complex task decomposing it into many simple tasks, that can be easily performed by individuals of the swarm. Coordinated realization of these simple tasks while adhering to a pre-defined distribution of execution, allows for the achievement of the original complex task. The problem of task allocation arises from the need of assigning tasks to individuals in a coordinated fashion, allowing a good management of the swarm. Task allocation is a dynamic process because it requires a continuous adjustment in response to changes in the environment, the swarm configuration and/or the performance of the swarm. Swarm robotics emerges from this context of collective cooperation applied to swarms of real robots. In this approach, complex problems are solved by performing complex tasks using swarms of simple robots, with a limited processing and communication capabilities. Aiming at achieving flexibility and reliability, the allocation should emerge as a result of a distributed process. With the decentralization of the problem and the increasing number of robots in the swarm, the allocation process acquires a high complexity. Thus, the problem of task allocation can be characterized as an optimization process that assigns tasks to robots, so that the desired proportion is met at the end of the optimization process, find the desired solution. In this dissertation, we propose two algorithms that follow different to the problem of dynamic task allocation approaches: one is local and the other global. The algorithm for dynamic allocation of tasks with a local approach (ADTL) updates the task assignment of each robot based on a deterministic assessment of the current knowledge it has so far about the tasks allocated to the other robots of the swarm. The algorithm for dynamic task allocation with a global approach (ADTG) updates the allocation of tasks based on a swarm optimization process, inspired by PSO (Particle swarm optimization). In ADTG, each robot has a possible solution to the swarm allocation, which is continuously updated through the exchange of information between the robots. The allocations are evaluated for their fitness in meeting the goal proportion. When the allocation of highest fitness in the swarm is identified, all robots of the swarm are allocated to the tasks defined by this allocation. The proposed algorithms were implemented on swarms of different arrangements of real robots demonstrating their efficacy, robustness and efficiency, certified by obtained the results.
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Lakshminarayanan, Srivathsan. "Nature Inspired Grey Wolf Optimizer Algorithm for Minimizing Operating Cost in Green Smart Home." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1438102173.
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Gross, Roderich. "Self-assembling robots." Doctoral thesis, Universite Libre de Bruxelles, 2007. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210656.
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We look at robotic systems made of separate discrete components that, by self-assembling, can organize into physical structures of growing size. We review 22 such systems, exhibiting components ranging from passive mechanical parts to mobile<p>robots. We present a taxonomy of the systems, and discuss their design and function. We then focus on a particular system, the swarm-bot. In swarm-bot, the components that assemble are self-propelled modules that are fully autonomous in power, perception, computation, and action. We examine the additional capabilities and functions self-assembly can offer an autonomous group of modules for the accomplishment of a concrete task: the transport of an object. The design of controllers is accomplished in simulation using<p>techniques from biologically-inspired computing. We show that self-assembly can offer adaptive value to groups that compete in an artificial evolution based on their fitness in task performance. Moreover, we investigate mechanisms that facilitate the design of self-assembling systems. The controllers are transferred to the physical swarm-bot system, and the capabilities of self-assembly and object transport are extensively evaluated in a range of different environments. Additionally, the controller for self-assembly is transferred and evaluated on a different robotic system, a super-mechano colony. Given the breadth and quality of the results obtained, we can say that the swarm-bot qualifies as the current state of the art in self-assembling robots. Our work supplies some initial evidence (in form of simulations and experiments with the swarm-bot) that self-assembly can offer robotic systems additional capabilities and functions useful for the accomplishment of concrete tasks.<p><br>Doctorat en Sciences de l'ingénieur<br>info:eu-repo/semantics/nonPublished
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Lakshminarayanan, Srinivasan. "Nature Inspired Discrete Integer Cuckoo Search Algorithm for Optimal Planned Generator Maintenance Scheduling." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1438101954.
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Kanade, Parag M. "Fuzzy ants as a clustering concept." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000397.
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