Relevant bibliographies by topics / Artificial Bee Colony Algorithms

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Academic literature on the topic 'Artificial Bee Colony Algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Artificial Bee Colony Algorithms"

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Patel, Subhash, and Rajesh A. Thakker. "Parameter Space Exploration for Analog Circuit Design Using Enhanced Bee Colony Algorithm." Journal of Circuits, Systems and Computers 28, no. 09 (August 2019): 1950153. http://dx.doi.org/10.1142/s0218126619501536.

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In this work, novel swarm optimization algorithm based on the Artificial Bee Colony (ABC) algorithm called Enhanced Artificial Bee Colony (EABC) algorithm is proposed for the design and optimization of the analog CMOS circuits. The new search strategies adopted improve overall performance of the proposed algorithm. The performance of EABC algorithm is compared with other competitive algorithms such as ABC, GABC (G-best Artificial Bee Colony Algorithm) and MABC (Modified Artificial Bee Colony Algorithm) by designing three CMOS circuits; Two-stage operational amplifier, low-voltage bulk driven OTA and second generation low-voltage current conveyor in 0.13 [Formula: see text]m and 0.09[Formula: see text][Formula: see text]m CMOS technologies. The obtained results clearly indicate that the performance of EABC algorithm is better than other mentioned algorithms and it can be an effective approach for the automatic design of the analog CMOS circuits.
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Minetti, Gabriela, and Carolina Salto. "Artificial Bee Colony Algorithm Improved with Evolutionary Operators." Journal of Computer Science and Technology 18, no. 02 (October 4, 2018): e13. http://dx.doi.org/10.24215/16666038.18.e13.

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In this paper, we design, implement, and analysis the replacement of the method to create new solutions in artificial bee colony algorithm by recombination operators, since the original method is similar to the recombination process used in evolutionary algorithms. For that purpose, we present a systematic investigation of the effect of using six different recombination operators for real-coded representations at the employed bee step. All the analysis is carried out using well known test problems. The experimental results suggest that the method to generate a new candidate food position plays an important role in the performance of the algorithm. Computational results and comparisons show that three of the six proposed algorithms are very competitive with the traditional bee colony algorithm.
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Qin, Quande, Shi Cheng, Qingyu Zhang, Li Li, and Yuhui Shi. "Artificial Bee Colony Algorithm with Time-Varying Strategy." Discrete Dynamics in Nature and Society 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/674595.

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Artificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a proper balance between these two conflictive factors, this paper proposed a novel ABC variant with a time-varying strategy where the ratio between the number of employed bees and the number of onlooker bees varies with time. The linear and nonlinear time-varying strategies can be incorporated into the basic ABC algorithm, yielding ABC-LTVS and ABC-NTVS algorithms, respectively. The effects of the added parameters in the two new ABC algorithms are also studied through solving some representative benchmark functions. The proposed ABC algorithm is a simple and easy modification to the structure of the basic ABC algorithm. Moreover, the proposed approach is general and can be incorporated in other ABC variants. A set of 21 benchmark functions in 30 and 50 dimensions are utilized in the experimental studies. The experimental results show the effectiveness of the proposed time-varying strategy.
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Verma, Balwant Kumar, and Dharmender Kumar. "A review on Artificial Bee Colony algorithm." International Journal of Engineering & Technology 2, no. 3 (June 21, 2013): 175. http://dx.doi.org/10.14419/ijet.v2i3.1030.

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In recent years large number of algorithms based on the swarm intelligence has been proposed by various researchers. The Artificial Bee Colony (ABC) algorithm is one of most popular stochastic, swarm based algorithm proposed by Karaboga in 2005 inspired from the foraging behavior of honey bees. In short span of time, ABC algorithm has gain wide popularity among researchers due to its simplicity, easy to implementation and fewer control parameters. Large numbers of problems have been solved using ABC algorithm such as travelling salesman problem, clustering, routing, scheduling etc. the aim of this paper is to provide up to date enlightenment in the field of ABC algorithm and its applications.
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Balasubramani, Kamalam, and Karnan Marcus. "A Comprehensive review of Artificial Bee Colony Algorithm." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 5, no. 1 (June 23, 2013): 15–28. http://dx.doi.org/10.24297/ijct.v5i1.4382.

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The Artificial Bee Colony (ABC) algorithm is a stochastic, population-based evolutionary method proposed by Karaboga in the year 2005. ABC algorithm is simple and very flexible when compared to other swarm based algorithms. This method has become very popular and is widely used, because of its good convergence properties. The intelligent foraging behavior of honeybee swarm has been reproduced in ABC.Numerous ABC algorithms were developed based on foraging behavior of honey bees for solving optimization, unconstrained and constrained problems. This paper attempts to provide a comprehensive survey of research on ABC. A system of comparisons and descriptions is used to designate the importance of ABC algorithm, its enhancement, hybrid approaches and applications.
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Xiao, Ren Bin, and Ying Cong Wang. "Research on Cellular Artificial Bee Colony Algorithm and its Computational Experiments." Applied Mechanics and Materials 284-287 (January 2013): 3168–72. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3168.

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It is the research hotspot for evolutionary algorithms to solve the contradiction between exploration and exploitation. Cellular artificial bee colony (CABC) algorithm is proposed by combining cellular automata with artificial bee colony algorithm from the perspective of the neighborhood in this paper. Each bee in the population structure defined in CABC has a fixed position and can only interact with bees in its neighborhood. The overlap between neighborhoods of different bees may make a bee an employed bee in one neighborhood and an onlooker bee in another neighborhood and vice versa, which increases the diversity of the population. The neighborhood and evolutionary rule help to control the selection pressure effectively, and the improved search mechanism in artificial bee colony algorithm is proposed to enhance the local search ability. The experimental results tested on four benchmark functions show that CABC can further balance the relationship between exploration and exploitation when compared with three ABC-based algorithms.
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Sharma, Harish, Jagdish Chand Bansal, K. V. Arya, and Kusum Deep. "Dynamic Swarm Artificial Bee Colony Algorithm." International Journal of Applied Evolutionary Computation 3, no. 4 (October 2012): 19–33. http://dx.doi.org/10.4018/jaec.2012100102.

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Artificial Bee Colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over test problems as well as real world optimization problems. This paper presents an attempt to modify ABC to make it less susceptible to stick at local optima and computationally efficient. In the case of local convergence, addition of some external potential solutions may help the swarm to get out of the local valley and if the algorithm is taking too much time to converge then deletion of some swarm members may help to speed up the convergence. Therefore, in this paper a dynamic swarm size strategy in ABC is proposed. The proposed strategy is named as Dynamic Swarm Artificial Bee Colony algorithm (DSABC). To show the performance of DSABC, it is tested over 16 global optimization problems of different complexities and a popular real world optimization problem namely Lennard-Jones potential energy minimization problem. The simulation results show that the proposed strategies outperformed than the basic ABC and three recent variants of ABC, namely, the Gbest-Guided ABC, Best-So-Far ABC and Modified ABC.
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Zou, Wenping, Yunlong Zhu, Hanning Chen, and Xin Sui. "A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm." Discrete Dynamics in Nature and Society 2010 (2010): 1–16. http://dx.doi.org/10.1155/2010/459796.

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Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO), and its cooperative version (CPSO) are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.
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Sharma, Tarun Kumar, and Millie Pant. "Differential Operators Embedded Artificial Bee Colony Algorithm." International Journal of Applied Evolutionary Computation 2, no. 3 (July 2011): 1–14. http://dx.doi.org/10.4018/jaec.2011070101.

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Artificial Bee Colony (ABC) is one of the most recent nature inspired (NIA) algorithms based on swarming metaphor. Proposed by Karaboga in 2005, ABC has proven to be a robust and efficient algorithm for solving global optimization problems over continuous space. However, it has been observed that the structure of ABC is such that it supports exploration more in comparison to exploitation. In order to maintain a balance between these two antagonist factors, this paper suggests incorporation of differential evolution (DE) operators in the structure of basic ABC algorithm. The proposed algorithm called DE-ABC is validated on a set of 10 benchmark problems and the numerical results are compared with basic DE and basic ABC algorithm. The numerical results indicate that the presence of DE operators help in a significant improvement in the performance of ABC algorithm.
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Karaboga, Dervis. "Artificial bee colony algorithm." Scholarpedia 5, no. 3 (2010): 6915. http://dx.doi.org/10.4249/scholarpedia.6915.

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Dissertations / Theses on the topic "Artificial Bee Colony Algorithms"

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Marrè, Badalló Roser. "Implementation and Testing of Two Bee-Based Algorithms in Finite Element Model Updating." Thesis, KTH, Bro- och stålbyggnad, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-140846.

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Finite Element Model Updating has recently arisen as an issue of vast importance on the design, construction and maintenance of structures in civil engineering. Many algorithms have been proposed, developed and enhanced in order to accomplish the demands of the updating process, mainly to achieve computationally efficient programs and greater results.The present Master Thesis proposes two new algorithms to be used in Finite Element Model Updating: the Bees Algorithms (BA) and the Artificial Bee Colony algorithm (ABC). Both were first proposed in 2005, are based on the foraging behaviour of bees and have been proved to be efficient algorithms in other fields. The objective of this Master Thesis is, thus, to implement and to test these two newalgorithms in Finite Element Model Updating for a cantilever beam. The Finite Element Model and the algorithms are programmed, followed by the extraction of the experimental frequencies and the updating process. Results, comparison of these two methods and conclusions are given at the end of this report, as well as suggestions for further work.
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Hashim, Mohd Ruzaini. "Improved spiral dynamics and artificial bee colony algorithms with application to engineering problems." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/20175/.

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Lee, Jessica. "Vägplanering i dataspel med hjälp av Artificial Bee Colony Algorithm." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11044.

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Artificial Bee Colony Algorithm är en algoritm som tidigare tillämpats på många numeriska optimeringsproblem. Algoritmen är dock inte vanligt förekommande vad gäller vägplanering i dataspel. Detta arbete undersöker ifall algoritmen presterar bättre än A* på fyra olika testmiljöer eftersom A* är en av de mest använda algoritmerna för vägplanering i dataspel och således en bra referens. De aspekter som jämförs vid mätningarna är algoritmernas tidsåtgång samt längden på de resulterande vägarna. En riktad slumpgenerering av vägar har implementerats till algoritmen som gör att den inte fungerar på djupa återvändsgränder. Algoritmen har också en roulettehjulsselektion samt egenskapen att kunna generera slumpade grannvägar till de som skapats hittills. Resultaten visar att Artificial Bee Colony Algorithm presterar betydligt sämre än A* och att algoritmen därför inte är en bättre algoritm för vägplanering i dataspel. Algoritmen har dock potential till att prestera bättre och fungera på återvändsgränder om man förbättrar dess slumpgenerering av vägar.
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Idachaba, Unekwu Solomon. "A bio-inspired cache management policy for cloud computing environments using the artificial bee colony algorithm." Thesis, University of Kent, 2015. https://kar.kent.ac.uk/57856/.

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Caching has become an important technology in the development of cloud computing-based high-performance web services. Caches reduce the request-response latency experienced by users and reduce workload on backend databases. Caches need a high cache-hit rate to be fit for purpose, and this is dependent on the cache management policy used. Existing cache management policies do not prevent cache pollution and cache monopoly. This lack of prevention impacts negatively on cache hit rates. This work presents a Bio-inspired Community-based Caching (BCC) approach to address these two problems, by drawing intelligence from users' access behaviour using the Quantity and Quality Aware Artificial Bee Colony (Q2-ABC) clustering algorithm to achieve high cache-hit rates. Q2-ABC is a redesigned Artificial Bee Colony (ABC) algorithm which is also presented in this work. It optimizes the quality of clusters produced by addressing the repetition in metric space searches, probability-based effort distribution, and limit of abandonment problems inherent in ABC. To evaluate the performance of BCC, two sets of experiments were performed. In the first set of experiments, the quality of clusters identified by Q2-ABC was between 15% and 63% better than ABC. The performance of Q2-ABC comes with a cost: additional storage (a maximum of 300 bytes in this experiment) to store indexes of searched metric space. In the second set of experiments, the cache-hit rate achieved by BCC was between 0.7% and 55% better than the others across most of the test data used. The cost associated with BCC performance includes additional memory requirement-a total of 1.7Mb in this experiment-for storing generated intelligence and processor cycle overhead for generating intelligence. The implication of these results are that better quality clusters are produced by avoiding repeated searches within a metric space, and that high cache-hit rate can be achieved by managing caches intelligently, an alternative to expanding them as is conventional for Cloud Computing based services.
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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.
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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Matakas, Linas. "Dirbtinės bičių kolonijos algoritmai ir jų taikymai skirstymo uždaviniams spręsti." Bachelor's thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2013~D_20130729_150200-28811.

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Šiame darbe yra trumpai apžvelgiami dalelių spiečių sistemų algoritmai, skirstymo uždaviniai ir jų formuluotės, bei praktinės interpretacijos, plačiau apžvelgiami ir analizuojami dirbtinių bičių kolonijų algoritmai. Taip pat šiame darbe galima rasti dirbtinių bičių kolonijų algoritmo pritaikymą skirstymo uždaviniams spręsti, bei sukurtos programos skaičiavimo rezultatų analizę.
This paper consists of short descriptions of swarm systems algorithms, assigment problems and longer overview of artificial bee colony algorithms and it‘s analysis. Moreover, you can find an Artificial Bee Colony Algorithm's Application to one of an Assigment Problems and it's computational results analysis.
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Kavaliauskas, Donatas. "Dirbtinės bičių kolonijos algoritmai ir jų taikymai maršrutų optimizavimo uždaviniams spręsti." Bachelor's thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2013~D_20130729_153102-94516.

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Šiame darbe yra trumpai apžvelgiami dalelių spiečių sistemų algoritmai, maršrutų optimizavimo uždaviniai ir jų formuluotės, bei praktinės interpretacijos. Plačiau apžvelgiami dirbtinių bičių kolonijų algoritmai ir jų pritaikymas keliaujančio pirklio uždaviniams spręsti. Taip pat šiame darbe galima rasti dirbtinių bičių kolonijų algoritmo pritaikymą keliaujančio pirklio uždaviniams spręsti, bei sukurtos programos skaičiavimo rezultatų analizę.
This paper consists of short description of swarm systems algorithms, route optimisation problems overview and longer description of artificial bee colony algorithms adaptation for traveling salesman problem. Moreover, you can find an artificial bee colony algorithm's application to traveling salesman problem and analysis of computational results.
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SOUZA, Viviane Lucy Santos de. "Uma metodologia para síntese de circuitos digitais em FPGAs baseada em otimização multiobjetivo." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/17339.

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Submitted by Irene Nascimento (irene.kessia@ufpe.br) on 2016-07-12T18:32:53Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese_Final_bib.pdf: 4325542 bytes, checksum: 5cafa644d256b743ce0f06490e4d5920 (MD5)
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Atualmente, a evolução na arquitetura dos FPGAs (Field programable gate arrays) permite que os mesmos sejam empregados em aplicações que vão desde a prototipação rápida de circuitos digitais simples a coprocessadores para computação de alto desempenho. Entretanto, a utilização eficiente dessas arquiteturas é fortemente dependente, entre outros fatores, da ferramenta de síntese empregada. O desafio das ferramentas de síntese está em converter a lógica do projetista em circuitos que utilizem de maneira efetiva a área do chip, não degradem a frequência de operação e que, sobretudo, sejam eficientes em reduzir o consumo de energia. Nesse sentido, pesquisadores e grandes fabricantes de FPGA estão, frequentemente, desenvolvendo novas ferramentas com vistas a esses objetivos, que se caracterizam por serem conflitantes. O fluxo de síntese de projetos baseados em FPGAs engloba as etapas de otimização lógica, mapeamento, agrupamento, posicionamento e roteamento. Essas fases são dependentes, de forma que, otimizações nas etapas iniciais produzem impactos positivos nas etapas posteriores. No âmbito deste trabalho de doutorado, estamos propondo uma metodologia para otimização do fluxo de síntese, especificamente, nas etapas de mapeamento e agrupamento. Classicamente, a etapa de mapeamento é realizada mediante heurísticas que determinam uma solução para o problema, mas que, não permitem a busca por soluções ótimas, ou que beneficiam um objetivo em detrimento de outros. Desta forma, estamos propondo a utilização de uma abordagem multiobjetivo baseada em algoritmo genético e de uma abordagem multiobjetivo baseada em colônia artificial de abelhas que, associadas a heurísticas específicas do problema, permitem que sejam obtidas soluções de melhor qualidade e que resultam em circuitos finais com área reduzida, ganhos na frequência de operação e com menor consumo de potência dinâmica. Além disso, propomos uma nova abordagem de agrupamento multiobjetivo que se diferencia do estado da arte, por utilizar uma técnica de predição e por considerar características dinâmicas do problema, produzindo circuitos mais eficientes e que facilitam a tarefa das etapas de posicionamento e roteamento. Toda a metodologia proposta foi integrada ao fluxo acadêmico do VTR (Verilog to routing), um projeto código aberto e colaborativo que conta com múltiplos grupos de pesquisa, conduzindo trabalhos nas áreas de desenvolvimento de arquitetura de FPGAs e de novas ferramentas de síntese. Além disso, utilizamos como benchmark, um conjunto dos 20 maiores circuitos do MCNC (Microelectronics Center of North Carolina) que são frequentemente utilizados em pesquisas da área. O resultado do emprego integrado das ferramentas frutos da metodologia proposta permite a redução de importantes aspectos pós-roteamento avaliados. Em comparação ao estado da arte, são obtidas, em média, redução na área dos circuitos de até 19%, além da redução do caminho crítico em até 10%, associada à diminuição na potência dinâmica total estimada de até 18%. Os experimentos também mostram que as metodologias de mapeamento propostas são computacionalmente mais custosas em comparação aos métodos presentes no estado da arte, podendo ser até 4,7x mais lento. Já a metodologia de agrupamento apresentou pouco ou nenhum overhead em comparação ao metodo presente no VTR. Apesar do overhead presente no mapeamento, os métodos propostos, quando integrados ao fluxo completo, podem reduzir o tempo de execução da síntese em cerca de 40%, isto é o resultado da produção de circuitos mais simples e que, consequentemente, favorecem as etapas de posicionamento e roteamento.
Nowadays, the evolution of FPGAs (Field Programmable Gate Arrays) allows them to be employed in applications from rapid prototyping of digital circuits to coprocessor of high performance computing. However, the efficient use of these architectures is heavily dependent, among other factors, on the employed synthesis tool. The synthesis tools challenge is in converting the designer logic into circuits using effectively the chip area, while, do not degrade the operating frequency and, especially, are efficient in reducing power consumption. In this sense, researchers and major FPGA manufacturers are often developing new tools to achieve those goals, which are characterized by being conflicting. The synthesis flow of projects based on FPGAs comprises the steps of logic optimization, mapping, packing, placement and routing. These steps are dependent, such that, optimizations in the early stages bring positive results in later steps. As part of this doctoral work, we propose a methodology for optimizing the synthesis flow, specifically, on the steps of mapping and grouping. Classically, the mapping step is performed by heuristics which determine a solution to the problem, but do not allow the search for optimal solutions, or that benefit a goal at the expense of others. Thus, we propose the use of a multi-objective approach based on genetic algorithm and a multi-objective approach based on artificial bee colony that, combined with problem specific heuristics, allows a better quality of solutions are obtained, yielding circuits with reduced area, operating frequency gains and lower dynamic power consumption. In addition, we propose a new multi-objective clustering approach that differs from the state-of-the-art, by using a prediction technique and by considering dynamic characteristics of the problem, producing more efficient circuits and that facilitate the tasks of placement and routing steps . The proposal methodology was integrated into the VTR (Verilog to routing) academic flow, an open source and collaborative project that has multiple research groups, conducting work in the areas of FPGA architecture development and new synthesis tools. Furthermore, we used a set of the 20 largest MCNC (Microelectronics Center of North Carolina) benchmark circuits that are often used in research area. The results of the integrated use of tools based on the proposed methodology allow the reduction of important post-routing aspects evaluated. Compared to the stateof- the-art, are achieved, on average, 19% reduction in circuit area, besides 10% reduction in critical path, associated with 18% decrease in the total dynamic estimated power. The experiments also reveal that proposed mapping methods are computationally more expensive in comparison to methods in the state-of-the-art, and may even be 4.7x slower. However, the packing methodology presented little or no overhead compared to the method in VTR. Although the present overhead mapping, the proposed methods, when integrated into the complete flow, can reduce the running time of the synthesis by approximately 40%, which is the result of more simple circuits and which, consequently, favor the steps of placement and routing.
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Duarte, Grasiele Regina. "Um algoritmo inspirado em colônias de abelhas para otimização numérica com restrições." Universidade Federal de Juiz de Fora (UFJF), 2015. https://repositorio.ufjf.br/jspui/handle/ufjf/3544.

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Os problemas de otimização estão presentes em diversas áreas de atuação da sociedade e o uso de algoritmos bio-inspirados para a resolução de problemas complexos deste tipo vem crescendo constantemente. O Algoritmo Colônia de Abelhas Artificiais (ABC – do inglês Artificial Bee Colony) é um algoritmo bio-inspirado proposto em 2005 para a resolução de problemas de otimização multimodais e multidimensionais. O fenômeno natural que inspirou o desenvolvimento do ABC foi o comportamento inteligente observado em colônias de abelhas, mais especificamente no forrageamento. O ABC foi proposto inicialmente para ser aplicado na resolução de problemas sem restrições. Este trabalho avalia o desempenho do ABC quando aplicado na resolução de problemas de otimização com restrições. Para o tratamento das restrições, métodos de penalização serão incorporados ao ABC. São analisados diversos métodos de penalização, de diferentes tipos, com o objetivo de identificar com qual deles o algoritmo apresenta melhor desempenho. Além disto, são avaliadas possíveis limitações e cuidados que devem ser tomados ao combinar métodos de penalização ao ABC. O algoritmo proposto é avaliado através da resolução de problemas de otimização encontrados na literatura. Vários experimentos computacionais são realizados e gráficos e tabelas são gerados para demonstração dos resultados obtidos que também são discutidos.
Optimization problems are present in several areas of society and the use of bio-inspired algorithms to solve complex problems of this type has been growing constantly. The Artificial Bee Colony Algorithm (ABC) is a bio-inspired algorithm proposed in 2005 for solving multimodal and multidimensional optimization problems. The natural phenomenon that inspired the development of the ABC was intelligent behavior observed in bee colonies, more specifically in foraging. The ABC was initially proposed to be applied to solve unconstrained problems. This study evaluates the performance of ABC when applied in solving constrained optimization problems. For the treatment of constraints, penalty methods will be incorporated into the ABC. Several penalty methods, of different types, are analyzed with the goal of identifying which of these penalty methods offers better performance. Furthermore, possible limitations and care that should be taken when combining penalty methods to ABC are evaluated. The proposed algorithm is evaluated by solving optimization problems found in the literature. Several computational experiments are performed and graphs and tables are generated for demonstration of the obtained results which are also discussed.
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Vladimir, Bugarski. "Ekspertski sistem za upravljanje brodskom prevodnicom zasnovan na računarskoj inteligenciji." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2015. http://www.cris.uns.ac.rs/record.jsf?recordId=95378&source=NDLTD&language=en.

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U disertaciji je dato jedno rešenje automatskog operativnogupravljanja dvosmernom brodskom prevodnicom sa jednom komorom.Kreiran je ekspertski sistem zasnovan na rasplinutoj (fuzzy) logici.Upravljački sistem je testiran na modelu brodske prevodnice koji jekreiran na osnovu statističkih podataka o gustini saobraćaja nahidrosistemu DTD (Dunav-Tisa-Dunav), na osnovu tehničkedokumentacije brodske prevodnice i na osnovu razgovora saoperaterima. Sistem je zatim optimizovan globalnim algoritmimaoptimizacije. Dobijeno rešenje se pokazalo značajno bolje u poređenjusa standardnim algoritmima odluke.
This thesis presents a solution to automatic control of a two-way one-channelship lock. Expert system based on fuzzy logic is designed. This controlsystem is tested on model of ship lock created using statistical data oftransportation density on DTD (Danube-Tisa-Danube) channel, usingtechnical documentation of ship lock and interview with operators. Thesystem is further optimized with global optimization techniques. Givensolution proved to be significantly better than standard decision algorithms. 
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Books on the topic "Artificial Bee Colony Algorithms"

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Ünal, Muhammet. Optimization of PID Controllers Using Ant Colony and Genetic Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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P, Spaink H., Rozenberg Grzegorz, Kok Joost N, Back Th, Eiben Agoston E, and SpringerLink (Online service), eds. Bee-Inspired Protocol Engineering: From Nature to Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Topuz, Vedat, Muhammet Ünal, and Ayça Ak. Optimization of PID Controllers Using Ant Colony and Genetic Algorithms. Springer, 2012.

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Topuz, Vedat, Muhammet Ünal, Ayça Ak, and Hasan Erdal. Optimization of PID Controllers Using Ant Colony and Genetic Algorithms. Springer, 2014.

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Farooq, Muddassar. Bee-Inspired Protocol Engineering: From Nature to Networks. Springer, 2010.

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Book chapters on the topic "Artificial Bee Colony Algorithms"

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Badar, Altaf Q. H. "Artificial Bee Colony." In Evolutionary Optimization Algorithms, 115–36. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003206477-6.

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Akay, Bahriye, and Dervis Karaboga. "Artificial Bee Colony Algorithm." In Swarm Intelligence Algorithms, 17–30. First edition. | Boca Raton : Taylor and Francis, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-2.

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Okwu, Modestus O., and Lagouge K. Tartibu. "Artificial Bee Colony Algorithm." In Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications, 15–31. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61111-8_3.

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Kaveh, Ali, and Taha Bakhshpoori. "Artificial Bee Colony Algorithm." In Metaheuristics: Outlines, MATLAB Codes and Examples, 19–30. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04067-3_3.

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Jadon, Shimpi Singh, Jagdish Chand Bansal, Ritu Tiwari, and Harish Sharma. "Expedited Artificial Bee Colony Algorithm." In Advances in Intelligent Systems and Computing, 787–800. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1768-8_68.

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Kumar, Sandeep, Anand Nayyar, and Rajani Kumari. "Arrhenius Artificial Bee Colony Algorithm." In International Conference on Innovative Computing and Communications, 187–95. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2354-6_21.

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Cuevas, Erik, and Alma Rodríguez. "Artificial Bee Colony (ABC) Algorithm." In Metaheuristic Computation with MATLAB®, 183–200. First edition. | Boca Raton : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003006312-7.

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Xian, Zhengguang, Jun Xie, and Yanfei Wang. "Representative Artificial Bee Colony Algorithms: A Survey." In LISS 2012, 1419–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32054-5_201.

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Zhang, Di, and Hao Gao. "Global-Best Leading Artificial Bee Colony Algorithms." In 3rd EAI International Conference on Robotic Sensor Networks, 55–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46032-7_6.

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Liang, Wanying, Shuo Liu, Kang Zhou, Shiji Fan, Xuechun Shang, and Yanzi Yang. "Improved Discrete Artificial Bee Colony Algorithm." In Communications in Computer and Information Science, 581–97. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3425-6_46.

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Conference papers on the topic "Artificial Bee Colony Algorithms"

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Huang, Fuxin, Lijue Wang, and Chi Yang. "Ship Hull Form Optimization Using Artificial Bee Colony Algorithm." In SNAME Maritime Convention. SNAME, 2014. http://dx.doi.org/10.5957/smc-2014-t47.

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In this paper, artificial bee colony (ABC) algorithms are introduced to optimize ship hull forms for reduced drag. Two versions of ABC algorithm are used: one is the basic ABC algorithm, and the other is an improved artificial bee colony (IABC) algorithm. A recently developed fast flow solver based on the Neumann-Michell theory is used to evaluate the drag of the ship in the optimization process. The ship hull surface is represented by discrete triangular panels and modified using radial basis function interpolation method. The developed optimization algorithms are first validated by benchmark mathematical functions with different dimensions. They are then applied to the optimization of DTMB Model 5415 for reduced drag. Two optimal hull forms are obtained by the ABC and the IABC algorithms. A large drag reduction is obtained by both of the algorithms. The optimal hull form obtained by the IABC algorithm has larger drag reduction than that of the hull form from the ABC algorithm. The results show that two ABC algorithms can be used for optimizing ship hull forms and the IABC algorithm has better performance than the ABC algorithm for the tested case in ship hull form optimization.
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Zhang, Dongli, Xinping Guan, Yinggan Tang, and Yong Tang. "Modified Artificial Bee Colony Algorithms for Numerical Optimization." In 2011 3rd International Workshop on Intelligent Systems and Applications (ISA). IEEE, 2011. http://dx.doi.org/10.1109/isa.2011.5873266.

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Xiaojun Bi and Yanjiao Wang. "An improved artificial bee colony algorithm." In 2011 3rd International Conference on Computer Research and Development (ICCRD). IEEE, 2011. http://dx.doi.org/10.1109/iccrd.2011.5764108.

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"An improved artificial bee colony algorithm." In The 2nd World Conference on Humanities and Social Sciences. Francis Academic Press, 2018. http://dx.doi.org/10.25236/wchss.2017.06.

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Sharma, Harish, Sonal Sharma, and Sandeep Kumar. "Lbest Gbest Artificial Bee Colony algorithm." In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2016. http://dx.doi.org/10.1109/icacci.2016.7732158.

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Rajawat, Ankita, Nirmala Sharma, and Harish Sharma. "Elitism based artificial bee colony algorithm." In 2017 International Conference on Computing, Communication and Automation (ICCCA). IEEE, 2017. http://dx.doi.org/10.1109/ccaa.2017.8229802.

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Kang, Fei, Junjie Li, Haojin Li, Zhenyue Ma, and Qing Xu. "An Improved Artificial Bee Colony Algorithm." In 2010 2nd International Workshop on Intelligent Systems and Applications (ISA). IEEE, 2010. http://dx.doi.org/10.1109/iwisa.2010.5473452.

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Liu, Hongzhi, Liqun Gao, Xiangyong Kong, and Shuyan Zheng. "An improved artificial bee colony algorithm." In 2013 25th Chinese Control and Decision Conference (CCDC). IEEE, 2013. http://dx.doi.org/10.1109/ccdc.2013.6560956.

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Yi, Yujang, and Renjie He. "A Novel Artificial Bee Colony Algorithm." In 2014 6th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 2014. http://dx.doi.org/10.1109/ihmsc.2014.73.

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El-Abd, Mohammed. "Opposition-based artificial bee colony algorithm." In the 13th annual conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001576.2001592.

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Reports on the topic "Artificial Bee Colony Algorithms"

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Kanagavel, Rameshkumar, and Indragandhi Vairavasundaram. FPGA Implementation and Investigation of Hybrid Artificial Bee Colony Algorithm-based Single Phase Shunt Active Filter. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, May 2020. http://dx.doi.org/10.7546/crabs.2020.05.13.

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