Academic literature on the topic 'PSO (Particle Swarm Optimization) discrete'
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Journal articles on the topic "PSO (Particle Swarm Optimization) discrete"
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Wang, Bei Zhan, Xiang Deng, Wei Chuan Ye, and Hai Fang Wei. "Study on Discrete Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 220-223 (November 2012): 1787–94. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1787.
Full textAbstract:
The particle swarm optimization (PSO) algorithm is a new type global searching method, which mostly focus on the continuous variables and little on discrete variables. The discrete forms and discretized methods have received more attention in recent years. This paper introduces the basic principles and mechanisms of PSO algorithm firstly, then points out the process of PSO algorithm and depict the operation rules of discrete PSO algorithm. Various improvements and applications of discrete PSO algorithms are reviewed. The mechanisms and characteristics of two different discretized strategies are presented. Some development trends and future research directions about discrete PSO are proposed.
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Ting, T. O., H. C. Ting, and T. S. Lee. "Taguchi-Particle Swarm Optimization for Numerical Optimization." International Journal of Swarm Intelligence Research 1, no. 2 (2010): 18–33. http://dx.doi.org/10.4018/jsir.2010040102.
Full textAbstract:
In this work, a hybrid Taguchi-Particle Swarm Optimization (TPSO) is proposed to solve global numerical optimization problems with continuous and discrete variables. This hybrid algorithm combines the well-known Particle Swarm Optimization Algorithm with the established Taguchi method, which has been an important tool for robust design. This paper presents the improvements obtained despite the simplicity of the hybridization process. The Taguchi method is run only once in every PSO iteration and therefore does not give significant impact in terms of computational cost. The method creates a more diversified population, which also contributes to the success of avoiding premature convergence. The proposed method is effectively applied to solve 13 benchmark problems. This study’s results show drastic improvements in comparison with the standard PSO algorithm involving continuous and discrete variables on high dimensional benchmark functions.
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Wu, Hua Li, Jin Hua Wu, and Ai Li Liu. "Hybrid Discrete Particle Swarm Optimizer Algorithm for Traveling Salesman Problem." Advanced Materials Research 433-440 (January 2012): 4526–29. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4526.
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PSO has been widely used in continuous optimization problems, but in discrete domain the research and application is very little. By redefining the position and speed of particles and related operations, the discrete particle swarm algorithm can be constructed. Due to the weak capacity of local search of PSO and be easy to constringe the local optimum, it is combined with simulated annealing and the hybrid discrete PSO is constructed using the characteristics that simulated annealing can accept some ungraded solution under the control of certain probability,finally the algorithm is applied to solving the traveling salesman problem successfully. The simulation results show that the hybrid discrete PSO can get better optimization effect, which validates the effectiveness of the method.
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Zhang, Jun Ting, and Li Xia Qiao. "Optimization Mechanism Control Strategy of Vehicle Routing Problem Based on Improved PSO." Advanced Materials Research 681 (April 2013): 130–36. http://dx.doi.org/10.4028/www.scientific.net/amr.681.130.
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Traveling salesman problem based on vehicle routing problem in the case, according to the discrete domain specificity, redefine the problem domain to the mapping relationship between particles and related operation rules, and the introduction of self learning operator so that the PSO algorithm can deal with discrete problem. Vehicle Routing Problem (VRP) is research on how to plan the vehicles routes in order to save the transportation cost. Improved Particle Swarm Optimization (PSO) algorithm is proposed to solve the VRP in this paper. To improve the efficiency of the Particle Swarm Optimization, self-learning operator is constructed. Particles are re coded and operate rules are redefined to deal with the discrete problem of VRP. The effectiveness of the proposed algorithm is demonstrated by the simulations.
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Goudos, Sotirios K., Zaharias D. Zaharis, and Konstantinos B. Baltzis. "Particle Swarm Optimization as Applied to Electromagnetic Design Problems." International Journal of Swarm Intelligence Research 9, no. 2 (2018): 47–82. http://dx.doi.org/10.4018/ijsir.2018040104.
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Particle swarm optimization (PSO) is a swarm intelligence algorithm inspired by the social behavior of birds flocking and fish schooling. Numerous PSO variants have been proposed in the literature for addressing different problem types. In this article, the authors apply different PSO variants to common design problems in electromagnetics. They apply the Inertia Weight PSO (IWPSO), the Constriction Factor PSO (CFPSO), and the Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithms to real-valued optimization problems, i.e. microwave absorber design, and linear array synthesis. Moreover, the authors use discrete PSO optimizers such as the binary PSO (binPSO) and the Boolean PSO with a velocity mutation (BPSO-vm) in order to solve discrete-valued optimization problems, i.e. patch antenna design. Additionally, the authors apply and compare binPSO with different transfer functions to thinning array design problems. In the case of a multi-objective optimization problem, they apply two multi-objective PSO variants to dual-band base station antenna optimization for mobile communications. Namely, these are the Multi-Objective PSO (MOPSO) and the Multi-Objective PSO with Fitness Sharing (MOPSO-fs) algorithms. Finally, the authors conclude the paper by providing a discussion on future trends and the conclusion.
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Bratton, Dan, and Tim Blackwell. "A Simplified Recombinant PSO." Journal of Artificial Evolution and Applications 2008 (February 19, 2008): 1–10. http://dx.doi.org/10.1155/2008/654184.
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Simplified forms of the particle swarm algorithm are very beneficial in contributing to understanding how a particle swarm optimization (PSO) swarm functions. One of these forms, PSO with discrete recombination, is extended and analyzed, demonstrating not just improvements in performance relative to a standard PSO algorithm, but also significantly different behavior, namely, a reduction in bursting patterns due to the removal of stochastic components from the update equations.
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R. B., Madhumala, Harshvardhan Tiwari, and Devaraj Verma C. "Resource Optimization in Cloud Data Centers Using Particle Swarm Optimization." International Journal of Cloud Applications and Computing 12, no. 2 (2022): 1–12. http://dx.doi.org/10.4018/ijcac.305856.
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To meet the ever-growing demand for computational resources, it is mandatory to have the best resource allocation algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is used to address the resource optimization problem. Particle Swarm Optimization is suitable for continuous data optimization, to use in discrete data as in the case of Virtual Machine placement we need to fine-tune some of the parameters in Particle Swarm Optimization. The Virtual Machine placement problem is addressed by our proposed model called Improved Particle Swarm Optimization (IM-PSO), where the main aim is to maximize the utilization of resources in the cloud datacenter. The obtained results show that the proposed algorithm provides an optimized solution when compared to the existing algorithms.
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Huang, Dan Hua, and Su Wang. "An Improved Discrete Particle Swarm Optimization for Berth Scheduling Problem." Applied Mechanics and Materials 373-375 (August 2013): 1192–95. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1192.
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Berth scheduling operation is an important problem in container terminal. The mathematic model of this problem is described in this paper and an improved particle swarm optimization algorithm is introduced to obtain the optimal scheduling solution. A floating-point allocation rule is used to encode the particles in the discrete space. A local search method is combined with PSO to avoid precocity. Finally the experiments are done to prove the improved PSO in this paper can resolve the berth scheduling problem and get better solution and convergence speed than the basic PSO.
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Abdel-Kader, Rehab F. "Particle Swarm Optimization for Constrained Instruction Scheduling." VLSI Design 2008 (March 15, 2008): 1–7. http://dx.doi.org/10.1155/2008/930610.
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Instruction scheduling is an optimization phase aimed at balancing the performance-cost tradeoffs of the design of digital systems. In this paper, a formal framework is tailored in particular to find an optimal solution to the resource-constrained instruction scheduling problem in high-level synthesis. The scheduling problem is formulated as a discrete optimization problem and an efficient population-based search technique; particle swarm optimization (PSO) is incorporated for efficient pruning of the solution space. As PSO has proven to be successful in many applications in continuous optimization problems, the main contribution of this paper is to propose a new hybrid algorithm that combines PSO with the traditional list scheduling algorithm to solve the discrete problem of instruction scheduling. The performance of the proposed algorithms is evaluated on a set of HLS benchmarks, and the experimental results demonstrate that the proposed algorithm outperforms other scheduling metaheuristics and is a promising alternative for obtaining near optimal solutions to NP-complete scheduling problem instances.
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Feng, Hong Kui, Jin Song Bao, and Jin Ye. "Particle Swarm Optimization Combined with Ant Colony Optimization for the Multiple Traveling Salesman Problem." Materials Science Forum 626-627 (August 2009): 717–22. http://dx.doi.org/10.4028/www.scientific.net/msf.626-627.717.
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A lot of practical problem, such as the scheduling of jobs on multiple parallel production lines and the scheduling of multiple vehicles transporting goods in logistics, can be modeled as the multiple traveling salesman problem (MTSP). Due to the combinatorial complexity of the MTSP, it is necessary to use heuristics to solve the problem, and a discrete particle swarm optimization (DPSO) algorithm is employed in this paper. Particle swarm optimization (PSO) in the continuous space has obtained great success in resolving some minimization problems. But when applying PSO for the MTSP, a difficulty rises, which is to find a suitable mapping between sequence and continuous position of particles in particle swarm optimization. For overcoming this difficulty, PSO is combined with ant colony optimization (ACO), and the mapping between sequence and continuous position of particles is established. To verify the efficiency of the DPSO algorithm, it is used to solve the MTSP and its performance is compared with the ACO and some traditional DPSO algorithms. The computational results show that the proposed DPSO algorithm is efficient.
Dissertations / Theses on the topic "PSO (Particle Swarm Optimization) discrete"
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Urade, Hemlata S., and Rahila Patel. "Performance Evaluation of Dynamic Particle Swarm Optimization." IJCSN, 2012. http://hdl.handle.net/10150/283597.
Full textAbstract:
Optimization has been an active area of research for
several decades. As many real-world optimization
problems become increasingly complex, better
optimization algorithms are always needed.
Unconstrained optimization problems can be formulated
as a D-dimensional minimization problem as follows:
Min f (x) x=[x1+x2+……..xD]
where D is the number of the parameters to be optimized.
subjected to: Gi(x) <=0, i=1…q
Hj(x) =0, j=q+1,……m
Xε [Xmin, Xmax]D, q is the number of inequality
constraints and m-q is the number of equality constraints.
The particle swarm optimizer (PSO) is a relatively new
technique. Particle swarm optimizer (PSO), introduced by
Kennedy and Eberhart in 1995, [1] emulates flocking
behavior of birds to solve the optimization problems.<br>In this paper the concept of dynamic particle swarm
optimization is introduced. The dynamic PSO is different from
the existing PSO’s and some local version of PSO in terms of
swarm size and topology. Experiment conducted for benchmark
functions of single objective optimization problem, which shows
the better performance rather the basic PSO. The paper also
contains the comparative analysis for Simple PSO and Dynamic
PSO which shows the better result for dynamic PSO rather than
simple PSO.
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Cleghorn, Christopher Wesley. "A Generalized theoretical deterministic particle swarm model." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/33333.
Full textAbstract:
Particle swarm optimization (PSO) is a well known population-based search algorithm,
originally developed by Kennedy and Eberhart in 1995. The PSO has been utilized
in a variety of application domains, providing a wealth of empirical evidence for its
effectiveness as an optimizer. The PSO itself has undergone many alterations subsequent
to its inception, some of which are fundamental to the PSO's core behavior, others have
been more application specific. The fundamental alterations to the PSO have to a large
extent been a result of theoretical analysis of the PSO's particle's long term trajectory.
The most obvious example, is the need for velocity clamping in the original PSO. While
there were empirical fndings that suggested that each particle's velocity was increasing
at a rapid rate, it was only once a solid theoretical study was performed that the reason
for the velocity explosion was understood. There has been a large amount of theoretical
research done on the PSO, both for the deterministic model, and more recently for the
stochastic model.
This thesis presents an extension to the theoretical deterministic PSO model. Under the
extended model, conditions for particle convergence to a point are derived. At present
all theoretical PSO research is done under the stagnation assumption, in some form or
another. The analysis done under the stagnation assumption is one where the personal
best and neighborhood best are assumed to be non-changing. While analysis under the
stagnation assumption is very informative, it could never provide a complete description
of a PSO's behavior. Furthermore, the assumption implicitly removes the notion of
a social network structure from the analysis. The model used in this thesis greatly
weakens the stagnation assumption, by instead assuming that each particle's personal
best and neighborhood best can occupy an arbitrarily large number of unique positions.
Empirical results are presented to support the theoretical fndings.<br>Dissertation (MSc)--University of Pretoria, 2013.<br>gm2014<br>Computer Science<br>Unrestricted
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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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Amiri, Mohammad Reza Shams, and Sarmad Rohani. "Automated Camera Placement using Hybrid Particle Swarm Optimization." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3326.
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Context. Automatic placement of surveillance cameras' 3D models in an arbitrary floor plan containing obstacles is a challenging task. The problem becomes more complex when different types of region of interest (RoI) and minimum resolution are considered. An automatic camera placement decision support system (ACP-DSS) integrated into a 3D CAD environment could assist the surveillance system designers with the process of finding good camera settings considering multiple constraints. Objectives. In this study we designed and implemented two subsystems: a camera toolset in SketchUp (CTSS) and a decision support system using an enhanced Particle Swarm Optimization (PSO) algorithm (HPSO-DSS). The objective for the proposed algorithm was to have a good computational performance in order to quickly generate a solution for the automatic camera placement (ACP) problem. The new algorithm benefited from different aspects of other heuristics such as hill-climbing and greedy algorithms as well as a number of new enhancements. Methods. Both CTSS and ACP-DSS were designed and constructed using the information technology (IT) research framework. A state-of-the-art evolutionary optimization method, Hybrid PSO (HPSO), implemented to solve the ACP problem, was the core of our decision support system. Results. The CTSS is evaluated by some of its potential users after employing it and later answering a conducted survey. The evaluation of CTSS confirmed an outstanding satisfactory level of the respondents. Various aspects of the HPSO algorithm were compared to two other algorithms (PSO and Genetic Algorithm), all implemented to solve our ACP problem. Conclusions. The HPSO algorithm provided an efficient mechanism to solve the ACP problem in a timely manner. The integration of ACP-DSS into CTSS might aid the surveillance designers to adequately and more easily plan and validate the design of their security systems. The quality of CTSS as well as the solutions offered by ACP-DSS were confirmed by a number of field experts.<br>Sarmad Rohani: 004670606805 Reza Shams: 0046704030897
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Scheepers, Christiaan. "Multi-guided particle swarm optimization : a multi-objective particle swarm optimizer." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/64041.
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An exploratory analysis in low-dimensional objective space of the vector evaluated particle swarm optimization (VEPSO) algorithm is presented. A novel visualization technique is presented and applied to perform the exploratory analysis. The exploratory analysis together with a quantitative analysis revealed that the VEPSO algorithm continues to explore without exploiting the well-performing areas of the search space. A detailed investigation into the influence that the choice of archive implementation has on the performance of the VEPSO algorithm is presented. Both the Pareto-optimal front (POF) solution diversity and convergence towards the true POF is considered during the investigation. Attainment surfaces are investigated for their suitability in efficiently comparing two multi-objective optimization (MOO) algorithms. A new measure to objectively compare algorithms in multi-dimensional objective space, based on attainment surfaces, is presented. This measure, referred to as the porcupine measure, adapts the attainment surface measure by using a statistical test along with weighted intersection lines. Loosely based on the VEPSO algorithm, the multi-guided particle swarm optimization (MGPSO) algorithm is presented and evaluated. The results indicate that the MGPSO algorithm overcomes the weaknesses of the VEPSO algorithm and also outperforms a number of state of the art MOO algorithms on at least two benchmark test sets.<br>Thesis (PhD)--University of Pretoria, 2017.<br>Computer Science<br>PhD<br>Unrestricted
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Cleghorn, Christopher Wesley. "Particle swarm optimization : empirical and theoretical stability analysis." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/61265.
Full textAbstract:
Particle swarm optimization (PSO) is a well-known stochastic population-based search algorithm,
originally developed by Kennedy and Eberhart in 1995. Given PSO's success at solving numerous real world problems, a large number of PSO variants have been proposed. However, unlike the original PSO, most variants currently have little to no existing theoretical results. This lack of a theoretical underpinning makes it difficult, if not impossible, for practitioners to make informed decisions about the algorithmic setup. This thesis focuses on the criteria needed for particle stability, or as it is often refereed to as, particle convergence.
While new PSO variants are proposed at a rapid rate, the theoretical analysis often takes substantially longer to emerge, if at all. In some situation the theoretical analysis is not performed as the mathematical models needed to actually represent the PSO variants become too complex or contain intractable subproblems. It is for this reason that a rapid means of determining approximate stability criteria that does not require complex mathematical modeling is needed. This thesis presents an empirical approach for determining the stability criteria for PSO variants. This approach is designed to provide a real world depiction of particle stability by imposing absolutely no simplifying assumption on the underlying PSO variant being investigated. This approach is utilized to identify a number of previously unknown stability criteria.
This thesis also contains novel theoretical derivations of the stability criteria for both the fully informed PSO and the unified PSO. The theoretical models are then empirically validated utilizing the aforementioned empirical approach in an assumption free context.
The thesis closes with a substantial theoretical extension of current PSO stability research. It is common practice within the existing theoretical PSO research to assume that, in the simplest case, the personal and neighborhood best positions are stagnant. However, in this thesis, stability criteria are derived under a mathematical model where by the personal best and neighborhood best positions are treated as convergent sequences of random variables. It is also proved that, in order to derive stability criteria, no weaker assumption on the behavior of the personal and neighborhood best positions can be made. The theoretical extension presented caters for a large range of PSO variants.<br>Thesis (PhD)--University of Pretoria, 2017.<br>Computer Science<br>PhD<br>Unrestricted
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Djaneye-Boundjou, Ouboti Seydou Eyanaa. "Particle Swarm Optimization Stability Analysis." University of Dayton / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1386413941.
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SINGH, BHUPINDER. "A HYBRID MSVM COVID-19 IMAGE CLASSIFICATION ENHANCED USING PARTICLE SWARM OPTIMIZATION." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18864.
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COVID-19 (novel coronavirus disease) is a serious illness that has killed millions of
civilians and affected millions around the world. Mostly as result, numerous
technologies that enable both the rapid and accurate identification of COVID-19
illnesses will provide much assistance to healthcare practitioners. A machine learning-
based approach is used for the detection of COVID-19. In general, artificial intelligence
(AI) approaches have yielded positive outcomes in healthcare visual processing and
analysis. CXR is the digital image processing method that plays a vital role in the
analysis of Covid-19 disease. Due to the maximum accessibility of huge scale annotated
image databases, excessive success has been done using multiclass support vector
machines for image classification. Image classification is the main challenge to detect
medical diagnosis. The existing work used CNN with a transfer learning mechanism
that can give a solution by transferring information from GENETIC object recognition
tasks. The DeTrac method has been used to detect the disease in CXR images. DeTrac
method accuracy achieved 93.1~ 97 percent. In this proposed work, the hybridization
PSO+MSVM method has worked with irregularities in the CXR images database by
studying its group distances using a group or class mechanism. At the initial phase of
the process, a median filter is used for the noise reduction from the image. Edge
detection is an essential step in the process of COVID-19 detection. The canny edge
detector is implemented for the detection of edges in the chest x-ray images. The PCA
(Principal Component Analysis) method is implemented for the feature extraction
phase. There are multiple features extracted through PCA and the essential features are
optimized by an optimization technique known as swarm optimization is used for
feature optimization. For the detection of COVID-19 through CXR images, a hybrid
multi-class support vector machine technique is implemented. The PSO (particle swarm
optimization) technique is used for feature optimization. The comparative analysis of
various existing techniques is also depicted in this work. The proposed system has
achieved an accuracy of 97.51 percent, SP of 97.49 percent, and 98.0 percent of SN.
The proposed system is compared with existing systems and achieved better
performance and the compared systems are DeTrac, GoogleNet, and SqueezeNet.
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Leonard, Barend Jacobus. "Critical analysis of angle modulated particle swarm optimisers." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/61548.
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This dissertation presents an analysis of the angle modulated particle swarm optimisation (AMPSO) algorithm. AMPSO is a technique that enables one to solve binary optimisation problems with particle swarm optimisation (PSO), without any modifications to the PSO algorithm. While AMPSO has been successfully applied to a range of optimisation problems, there is little to no understanding of how and why the algorithm might fail. The work presented here includes in-depth theoretical and emprical analyses of the AMPSO algorithm in an attempt to understand it better. Where problems are identified, they are supported by theoretical and/or empirical evidence. Furthermore, suggestions are made as to how the identified issues could be overcome. In particular, the generating function is identified as the main cause for concern. The generating function in AMPSO is responsible for generating binary solutions. However, it is shown that the increasing frequency of the generating function hinders the algorithm’s ability to effectively exploit the search space. The problem is addressed by introducing methods to construct different generating functions, and to quantify the quality of arbitrary generating functions. In addition to this, a number of other problems are identified and addressed in various ways. The work concludes with an empirical analysis that aims to identify which of the various suggestions made throughout this dissertatioin hold substantial promise for further research.<br>Dissertation (MSc)--University of Pretoria, 2017.<br>Computer Science<br>MSc<br>Unrestricted
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Barla-Szabo, Daniel. "A study of gradient based particle swarm optimisers." Diss., University of Pretoria, 2010. http://hdl.handle.net/2263/29927.
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Gradient-based optimisers are a natural way to solve optimisation problems, and have long been used for their efficacy in exploiting the search space. Particle swarm optimisers (PSOs), when using reasonable algorithm parameters, are considered to have good exploration characteristics. This thesis proposes a specific way of constructing hybrid gradient PSOs. Heterogeneous, hybrid gradient PSOs are constructed by allowing the gradient algorithm to optimise local best particles, while the PSO algorithm governs the behaviour of the rest of the swarm. This approach allows the distinct algorithms to concentrate on performing the separate tasks of exploration and exploitation. Two new PSOs, the Gradient Descent PSO, which combines the Gradient Descent and PSO algorithms, and the LeapFrog PSO, which combines the LeapFrog and PSO algorithms, are introduced. The GDPSO represents arguably the simplest hybrid gradient PSO possible, while the LeapFrog PSO incorporates the more sophisticated LFOP1(b) algorithm, exhibiting a heuristic algorithm design and dynamic time step adjustment mechanism. The strong tendency of these hybrids to prematurely converge is examined, and it is shown that by modifying algorithm parameters and delaying the introduction of gradient information, it is possible to retain strong exploration capabilities of the original PSO algorithm while also benefiting from the exploitation of the gradient algorithms.<br>Dissertation (MSc)--University of Pretoria, 2010.<br>Computer Science<br>unrestricted
Books on the topic "PSO (Particle Swarm Optimization) discrete"
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López, Javier. Optimización multi-objetivo. Editorial de la Universidad Nacional de La Plata (EDULP), 2015. http://dx.doi.org/10.35537/10915/45214.
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Cuando hablamos de optimización en el ámbito de las ciencias de la computación hacemos referencia al mismo concepto coloquial asociado a esa palabra, la concreción de un objetivo utilizando la menor cantidad de recursos disponibles, o en una visión similar, la obtención del mejor objetivo posible utilizando todos los recursos con lo que se cuenta. Los métodos para encontrar la mejor solución (óptima) varían de acuerdo a la complejidad del problema enfrentado. Para problemas triviales, el cerebro humano posee la capacidad de resolverlos (encontrar la mejor solución) directamente, pero a medida que aumenta la complejidad del problema, se hace necesario contar con herramientas adicionales. En esta dirección, existe una amplia variedad de técnicas para resolver problemas complejos. Dentro de estas técnicas, podemos mencionar las técnicas exactas. Este tipo de algoritmos son capaces de encontrar las soluciones óptimas a un problema dado en una cantidad finita de tiempo. Como contrapartida, requiere que el problema a resolver cumpla con condiciones bastante restrictivas. Existen además un conjunto muy amplio de técnica aproximadas, conocidas como metaheurísticas. Estas técnicas se caracterizan por integrar de diversas maneras procedimientos de mejora local y estrategias de alto nivel para crear un proceso capaz de escapar de óptimos locales y realizar una búsqueda robusta en el espacio de búsqueda del problema. En su evolución, estos métodos han incorporado diferentes estrategias para evitar la convergencia a óptimos locales, especialmente en espacios de búsqueda complejos. Este tipo de procedimientos tienen como principal característica que son aplicables a cualquier tipo de problemas, sin requerir ninguna condición particular a cumplir por los mismos. Estas técnicas no garantizan en ningún caso la obtención de los valores óptimos de los problemas en cuestión, pero se ha demostrado que son capaces de alcanzar muy buenos valores de soluciones en períodos de tiempo cortos. Además, es posible aplicarlas a problemas de diferentes tipos sin mayores modificaciones, mostrando su robustez y su amplio espectro de uso. La mayoría de estas técnicas están inspiradas en procesos biológicos y/o físicos, y tratan de simular el comportamiento propio de estos procesos que favorecen la búsqueda y detección de soluciones mejores en forma iterativa. La más difundida de estas técnicas son los algoritmos genéticos, basados en el mecanismo de evolución natural de las especies. Existen diferentes tipos de problemas, y multitud de taxonomías para clasificar los mismos. En el alcance de este trabajo nos interesa diferenciar los problemas en cuanto a la cantidad de objetivos a optimizar. Con esta consideración en mente, surge una primera clasificación evidente, los problemas mono-objetivo, donde existe solo una función objetivo a optimizar, y los problemas multi-objetivo donde existe más de una función objetivo. En el presente trabajo se estudia la utilización de metaheurísticas evolutivas para la resolución de problemas complejos, con uno y con más de un objetivo. Se efectúa un análisis del estado de situación en la materia, y se proponen nuevas variantes de algoritmos existentes, validando que las mismas mejoran resultados reportados en la literatura. En una primera instancia, se propone una mejora a la versión canónica y mono-objetivo del algoritmo PSO, luego de un estudio detallado del patrón de movimientos de las partículas en el espacio de soluciones. Estas mejoras se proponen en las versiones de PSO para espacios continuos y para espacios binarios. Asimismo, se analiza la implementación de una versión paralela de esta técnica evolutiva. Como segunda contribución, se plantea una nueva versión de un algoritmo PSO multiobjetivo (MOPSO Multi Objective Particle Swarm Optimization) incorporando la posibilidad de variar dinámicamente el tamaño de la población, lo que constituye una contribución innovadora en problemas con mas de una función objetivo. Por último, se utilizan las técnicas representativas del estado del arte en optimización multi-objetivo aplicando estos métodos a la problemática de una empresa de emergencias médicas y atención de consultas domiciliarias. Se logró poner en marcha un proceso de asignación de móviles a prestaciones médicas basado en metaheurísticas, logrando optimizar el proceso de asignación de móviles médicos a prestaciones médicas en la principal compañía de esta industria a nivel nacional.
Book chapters on the topic "PSO (Particle Swarm Optimization) discrete"
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Wang, Feng-Sheng, and Li-Hsunan Chen. "Particle Swarm Optimization (PSO)." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_416.
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Badar, Altaf Q. H. "Different Applications of PSO." In Applying Particle Swarm Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_11.
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Cuevas, Erik, and Alma Rodríguez. "Particle Swarm Optimization (PSO) Algorithm." In Metaheuristic Computation with MATLAB®. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003006312-6.
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Fernández-Brillet, Lucas, Oscar Álvarez, and Juan Luis Fernández-Martínez. "The PSO Family: Application to the Portfolio Optimization Problem." In Applying Particle Swarm Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_7.
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Yarat, Serhat, Sibel Senan, and Zeynep Orman. "A Comparative Study on PSO with Other Metaheuristic Methods." In Applying Particle Swarm Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_4.
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Ehteram, Mohammad, Akram Seifi, and Fatemeh Barzegari Banadkooki. "Structure of Particle Swarm Optimization (PSO)." In Application of Machine Learning Models in Agricultural and Meteorological Sciences. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9733-4_2.
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Couceiro, Micael, and Pedram Ghamisi. "Fractional-Order Darwinian PSO." In Fractional Order Darwinian Particle Swarm Optimization. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19635-0_2.
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Gkaidatzis, Paschalis A., Aggelos S. Bouhouras, and Dimitris P. Labridis. "Application of PSO in Distribution Power Systems: Operation and Planning Optimization." In Applying Particle Swarm Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_17.
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Mohammed, Omar Hazem, and Mohammed Kharrich. "An Overview of the Performance of PSO Algorithm in Renewable Energy Systems." In Applying Particle Swarm Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_16.
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Mohammadazadeh, Ardahir, Mohammad Hosein Sabzalian, Oscar Castillo, Rathinasamy Sakthivel, Fayez F. M. El-Sousy, and Saleh Mobayen. "Neural Network Training Based Particle Swarm Optimization (PSO)." In Synthesis Lectures on Intelligent Technologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14571-1_6.
Full textConference papers on the topic "PSO (Particle Swarm Optimization) discrete"
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Li, H., and K. Chandrashekhara. "Structural Optimization of Laminated Composite Blade Using Particle Swarm Optimization." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-88313.
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Composite blades working underwater experience complicated loading conditions. Robust design of a composite blade for hydrokinetic applications should satisfy varying loading conditions and conservative failure evaluations. Blade manufacturing using composites requires extensive optimization studies in terms of composite materials, number of layers, stacking sequences, ply thickness and orientation. In the current study, particle swarm optimization (PSO) technique is adopted to conduct composite lay-up optimization for the turbine blade. Layer numbers, ply thickness and ply orientations are optimized using standard PSO (SPSO) to minimize weight. Composite failure criteria are applied using finite element method to generate the most conservative blade design. Based on the blade lay-up design with minimized weight, stacking sequence of the blade lay-up was optimized to maximum safety factor of the designed blade using permutation discrete PSO (PDPSO). To improve the efficiency of the algorithm, the concepts of valid/invalid exchange, and memory checking were introduced into PDPSO. Meanwhile, another discrete PSO using partially mapped crossover (PMX) technique was used to validate the simulation results optimized by PDPSO. A final composite blade design with minimized weight and maximized load-carrying capacity was presented.
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Tong, Weiyang, Souma Chowdhury, and Achille Messac. "A New Multi-Objective Mixed-Discrete Particle Swarm Optimization Algorithm." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35572.
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Complex system design problems tend to be high dimensional and nonlinear, and also often involve multiple objectives and mixed-integer variables. Heuristic optimization algorithms have the potential to address the typical (if not most) characteristics of such complex problems. Among them, the Particle Swarm Optimization (PSO) algorithm has gained significant popularity due to its maturity and fast convergence abilities. This paper seeks to translate the unique benefits of PSO from solving typical continuous single-objective optimization problems to solving multi-objective mixed-discrete problems, which is a relatively new ground for PSO application. The previously developed Mixed-Discrete Particle Swarm Optimization (MDPSO) algorithm, which includes an exclusive diversity preservation technique to prevent premature particle clustering, has been shown to be a powerful single-objective solver for highly constrained MINLP problems. In this paper, we make fundamental advancements to the MDPSO algorithm, enabling it to solve challenging multi-objective problems with mixed-discrete design variables. In the velocity update equation, the explorative term is modified to point towards the non-dominated solution that is the closest to the corresponding particle (at any iteration). The fractional domain in the diversity preservation technique, which was previously defined in terms of a single global leader, is now applied to multiple global leaders in the intermediate Pareto front. The multi-objective MDPSO (MO-MDPSO) algorithm is tested using a suite of diverse benchmark problems and a disc-brake design problem. To illustrate the advantages of the new MO-MDPSO algorithm, the results are compared with those given by the popular Elitist Non-dominated Sorting Genetic Algorithm-II (NSGA-II).
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Ma, Ming, Owen Hughes, and Tobin McNatt. "Ultimate Limit State Based Ship Structural Design Using Multi-Objective Discrete Particle Swarm Optimization." In ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/omae2015-41456.
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Multi-objective optimization problems consist of several objectives that must be handled simultaneously. These objectives usually conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. Genetic or evolution algorithms have been demonstrated to be particularly effective to determine excellent solutions to these problems. Among many algorithms, the particle swarm optimization (PSO) has been found to be faster with less computational overhead. In this paper a multi-objective discrete particle swarm optimization is formulated and used to optimize a large and complex thin-wall structure on the basis of weight, safety and cost. The structure weight and cost are calculated using realistic finite element models. The design process has two stages: (1) the actual stresses are obtained by finite element analysis of the full ship, (2) for a midship segment of the ship (referred to as a “control cluster”) the structural safety is evaluated using the ALPS/ULSAP set of ultimate limit state criteria, and then the segment is optimized using any suitable optimization method (in this paper, the PSO method). Both stages involve iteration, but the process is arranged so as to keep the number of full ship finite element analyses to a minimum. The complete design process is illustrated for a 200,000 ton oil tanker. The numerical results show that the PSO method is very useful to perform ultimate strength based ship structural optimization with multi-objectives, namely minimization of the structural weight and cost and maximization of structural safety. The example also demonstrates that the proper definition of boundary conditions and design load cases is of paramount importance for design optimization.
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Coelho, Leandro dos Santos, and Viviana Cocco Mariani. "A Multivariable Coupling Design for Variable Structure Control Using Particle Swarm Optimization." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-15556.
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This paper presents a new discrete-time sliding-mode control design for multiple-input multi-output (MIMO) systems with tuning parameters by particle swarm optimization (PSO). PSO is a kind of evolutionary algorithm based on a population of individuals and motivated by the simulation of social behavior instead of the survival of the fittest individual. Several control algorithms are presented, two decoupling design and six new approaches of the coupling design of sliding-mode control without the necessity of calculate the process interactor matrix. SMC needs a design tool for parameter configuration and efficient practically to deal with multivariable processes. Simulations are carried out using both decoupling and coupling discrete-time SMC designs. Results shown that the new proposed approach for designing the discrete-time coupling SMC is a powerful tool and it performs better than the decoupling design, usually utilized in MIMO process. The simulations are assessed on a robotic manipulator of two degree-of-freedom (2-DOF), that constitute a MIMO nonlinear coupling dynamic system, with treatment of payload mass and link length variations. Simulation results show that the application of this control strategy effectively improve the trajectory tracking precision of position and velocity variables.
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Kim, Tae-Uk. "Buckling Load Maximization of Composite Laminates Using Particle Swarm Optimization With Various Constraints." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-65312.
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The stacking sequence of composite laminates is designed to have maximum buckling load using the particle swarm optimization (PSO) algorithm. The original PSO algorithm is modified to handle the discrete ply angles and the constraints such as stiffness and 4-ply contiguity requirements. For this, the augmented Lagrange multiplier (ALM) method is incorporated into the PSO algorithm. For the verification of the algorithm, the benchmarking problems are solved and the results are compared with the ones from the genetic algorithm or the analytic solutions. And then the laminates under in-plane compressive loadings are optimized for maximum buckling load considering the various constraints. The numerical results show that the algorithm finds the optimum with relatively small number of iterations with satisfying the constraints explicitly. Considering its advantage of derivative-free and simple procedures, the proposed algorithm can be applied to more complex models coupled with finite element analysis and various constraints.
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Wang, Haikun, Hong-Zhong Huang, Huanwei Xu, Zhonglai Wang, and Xiaoling Zhang. "Sequential Particle Swarm Optimization and Reliability Assessment of Planar-Type Voice Coil Motor." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12979.
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Planar-type voice coil motor (VCM) is a key component in ultra-precision motion of fine stage of lithography machine. The reliability-based design optimization (RBDO) method given in this work provides a novel criterion to ensure performance of Lorentz motors by evaluating the reliability of force constant. To solve the reliability based design optimization problem in discrete space with the speed of decoupled loop in sequential optimization and reliability assessment (SORA) for global solution, a Sequential Particle Swarm Optimization and Reliability Assessment method is proposed. The reliability boundary shift is put into penalty function for constrained optimization in fitness evaluation of particle swarm optimization (PSO). The presented optimization design model, the geometric parameters of the studied Planar-type VCM in finite element model are treated as design variables whereas the thrust force constant is an output quantity of interests. By using electromagnetic analysis, the desired requirements of Lorentz motors are verified.
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Chowdhury, Souma, Achille Messac, and Ritesh Khire. "Comprehensive Product Platform Planning (CP3) Using Mixed-Discrete Particle Swarm Optimization and a New Commonality Index." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70954.
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A product family with a common platform paradigm can increase the flexibility and responsiveness of the product-manufacturing process and help take away market share from competitors that develop one product at a time. The recently developed Comprehensive Product Platform Planning (CP3) method allows (i) the formation of sub-families of products, and (ii) the simultaneous identification and quantification of platform/scaling design variables. The CP3 model is founded on a generalized commonality matrix representation of the product-platform-plan. In this paper, a new commonality index is developed and introduced in CP3 to simultaneously account for the degree of inter-product commonalities and for the overlap between groups of products sharing different platform variables. To maximize both the performance of the product family and the new commonality measure, we develop and apply an advanced mixed-discrete Particle Swarm Optimization (MDPSO) algorithm. In the MDPSO algorithm, the discrete variables are updated using a deterministic nearest-feasible-vertex criterion after each iteration of the conventional PSO. Such an approach is expected to avoid the undesirable discrepancy in the rate of evolution of discrete and continuous variables. To prevent a premature stagnation of solutions (likely in conventional PSO), while solving the high dimensional MINLP problem presented by CP3, we introduce a new adaptive diversity-preservation technique. This technique first characterizes the population diversity and then applies a stochastic update of the discrete variables based on the estimated diversity measure. The potential of the new CP3 optimization methodology is illustrated through its application to design a family of universal electric motors. The optimized platform plans provide helpful insights into the importance of accounting for the overlap between different product platforms, when quantifying the effective commonality in the product family.
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Abdollahirad, Mahdi, Arcan Yanik, and Unal Aldemir. "Wavelet PSO-Based LQR Algorithm for Optimal Structural Control Using Active Tuned Mass Dampers." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46140.
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This study presents a new method to find the optimal control forces for active tuned mass damper. The method uses three algorithms: discrete wavelet transform (DWT), particle swarm optimization (PSO), and linear quadratic regulator (LQR). DWT is used to obtain the local energy distribution of the motivation over the frequency bands. PSO is used to determine the gain matrices through the online update of the weighting matrices used in the LQR controller while eliminating the trial and error. The method is tested on a 10-story structure subject to several historical pulse-like near-fault ground motions. The results indicate that the proposed method is more effective at reducing the displacement response of the structure in real time than conventional LQR controllers.
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José da Silva, Diego, Edmarcio Antonio Belati, and Eduardo Werley S. Ângelos. "PSO-BE: Um Eficiente Algoritmo para Alocação e Dimensionamento de Bancos de Capacitores em Redes de Distribuição." In Simpósio Brasileiro de Sistemas Elétricos - SBSE2020. sbabra, 2020. http://dx.doi.org/10.48011/sbse.v1i1.2341.
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Este artigo apresenta uma eficiente metodologia híbrida composta pela metaheurística Particle Swarm Optimization (PSO) e uma Busca Exaustiva (BE) limitada, denominada PSO-BE. A metodologia foi aplicada ao problema de alocação e dimensionamento de banco de capacitores em redes de distribuição de energia, objetivando a minimização das perdas ativas considerando valores discretos de bancos de capacitores, sendo a alocação realizada pelo PSO, e o dimensionamento pela BE. A busca pela solução do problema de forma separada favorece a obtenção da solução ótima, o que foi comprovado em testes em sistemas de distribuição de 69 e 84 barras. A técnica proposta apresentou vantagens em relação a outras metodologias apresentadas na literatura especializada.
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Kul'ment'ev, Аlexander. "Artificial intelligence optimization method for nuclear fuel triso-elements in high-temperature reactor." In IXth INTERNATIONAL SAMSONOV CONFERENCE “MATERIALS SCIENCE OF REFRACTORY COMPOUNDS”. Frantsevich Ukrainian Materials Research Society, 2024. http://dx.doi.org/10.62564/m4-ak2225.
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In the present era nuclear energy, has unique advantages compared to other energy sources. Now significant research and development related to TRISO-coated fuels is underway worldwide as part of the activities of the Generation IV International Forum on Very-High-Temperature Reactors. The focus is largely on extending the capabilities of the TRISO-coated fuel system for higher operating temperatures (1250°C) and higher burnups (10 – 20 %). Of greatest concern is the influence of higher fuel temperatures and burnups on fission product interactions with the SiC layer leading to the release of fission products. One of the possible solution consist in addition additional layers with special properties. For example, to prevent the corrosion of the SiC layer by fission product palladium, several types of new combinations of the coating layers have been proposed and tested. The idea is to add a layer that traps palladium by chemical reaction inside the SiC layer. Earlier several kinds of additional layers have been selected: an SiC + PyC layer and an SiC layer. For optimization of TRISO particle it is necessary to determine the number of additional layers, their thickness and composition. This is combinatorial optimization problem (continuous + discrete). Traditional methods rely on manual adjustment and human experience, which is inefficient and difficult to obtain the optimal solution. Therefore it is necessary to develop an automated design method. In the present report variant of such method is proposed based on artificial intelligence approach. There are several meta-heuristic algorithms such as genetic algorithm, neural network and particle swarm optimization algorithm (PSO) which have the ability to solve continuous, discrete and combinatorial optimization problems. Namely PSO algorithm looks especially attractive. Early this method was proven to be reliable and effective in nuclear power problems by applying it in designing a Savannah marine reactor shielding.
Reports on the topic "PSO (Particle Swarm Optimization) discrete"
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Styling Parameter Optimization of the Type C Recreational Vehicle Air Drag. SAE International, 2021. http://dx.doi.org/10.4271/2021-01-5094.
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Recreational vehicles have a lot of potential consumers in China, especially the type C recreational vehicle is popular among consumers due to its advantages, prompting an increase in the production and sales volumes. The type C vehicle usually has a higher air drag than the common commercial vehicles due to its unique appearance. It can be reduced by optimizing the structural parameters, thus the energy consumed by the vehicle can be decreased. The external flow field of a recreational vehicle is analyzed by establishing its computational fluid dynamic (CFD) model. The characteristic of the RV’s external flow field is identified based on the simulation result. The approximation models of the vehicle roof parameters and air drag and vehicle volume are established by the response surface method (RSM). The vehicle roof parameters are optimized by multi-objective particle swarm optimization (MO-PSO). According to the comparison, the air drag is reduced by 2.89% and the vehicle volume is increased by 0.36%. For the RV, the proper geometry parameters can increase the inner space of the vehicle while reducing the air drag.
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RESEARCH ON DATA-DRIVEN INTELLIGENT DESIGN METHOD FOR ENERGY DISSIPATOR OF FLEXIBLE PROTECTION SYSTEMS. The Hong Kong Institute of Steel Construction, 2024. https://doi.org/10.18057/ijasc.2024.20.4.6.
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The brake ring, an essential buffer and energy dissipator within flexible protection systems for mitigating dynamic impacts from rockfall collapses, presents notable design challenges due to its significant deformation and strain characteristics. This study introduces a highly efficient and precise neural network model tailored for the design of brake rings, utilizing BP neural networks in conjunction with Particle Swarm Optimization (PSO) algorithms. The paper studies the key geometric parameters, including ring diameter, tube diameter, wall thickness, and aluminum sleeve length, with performance objectives centered on starting load, maximum load, and energy dissipation. A comprehensive dataset comprising 576 samples was generated through the integration of full-scale tests and simulations, which facilitated the training of the neural network for accurate forward predictions linking physical parameters to performance outcomes. Furthermore, a PSO-based reverse design model was developed to enable effective back-calculation from desired performance outcomes to specific geometric configurations. The BP neural network exhibited high accuracy, evidenced by a fit of 0.991, and the mechanical performance of the designed products aligned with target values in over 90% of cases, with all engineering errors remaining within acceptable limits. The proposed method significantly reduces the design time to under 5 seconds, thereby vastly improving efficiency in comparison to traditional approaches. This advancement offers a rapid and reliable reference for the design of critical components in flexible protection systems.