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About 10,000 papers for “Particle swarm optimization”

Particle Swarm Optimization

M. B. Shanthi, D. Komagal Meenakshi, Prem Kumar Ramesh · 2018

The advancement and research in artificial intelligence (AI) and evolutionary computing have led to the invention of swarm intelligence (SI) techniques. SI works on the analogy of collective and collaborative behavior of the social species in nature. Self-organization, global ordering, and coordination, which result from interactive communication in the swarm, are the key features of the SI system, which is exploited for optimization in computational problems. Particle swarm optimization (PSO) is one of the most popular SI-based optimization algorithms inspired by social behavior observed in animals and insects. Each member in the swarm is treated as a particle. The collective intelligence of these decentralized particles is attained by cooperation and learning. For decades, PSO has been considered as one of the most efficient optimization techniques because of its simplicity in implementation, the number of parameters used for manipulation, quick convergence, and scalability. The main objective of this chapter is to provide a comprehensive investigation of PSO and its variants like unified PSO, memetic PSO, vector evaluated PSO, composite PSO to name a few. The chapter also focuses on the relevance of PSO in real-world applications.

Cited by 29

Particle Swarm Optimization

Protozoic Behaviour, Ajith Abraham · InTech eBooks · 2009

Abstract—Nature inspired algorithms implement successful optimization and adaptation strategies observed in the nature. Various bio-inspired algorithms mimic the behavioural patterns of plants, animals, their communities and their evolution. Surpris-ingly, the behavioural patterns and survival strategies of protozoa, one of the most prevalent and successful species on Earth, did not receive significant attention from the bio-inspired computing community until present time. This study proposes a new variant of Particle Swarm Optimization incorporating behaviour inspired by protozoa and evaluates the performance of such an algorithm on a set of well known test functions. Index Terms—Bio-inspired algorithms; particle swarm opti-mization; protozoic behaviour I.

Cited by 296

Particle Swarm Optimization

Gerhard Venter, Jaroslaw Sobieszczanski‐Sobieski · 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference · 2002

The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm, Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.

Cited by 165

Particle Swarm Optimization

Shafquat Rana, Md Sarwar, Anwar Shahzad Siddiqui et al. · 2023

Particle swarm optimization (PSO) has gained its importance over last 20 years and has been proven successful in many domains and disciplines of science and technology, as well as in other fields. It has shown its ability in optimizing various complex problems in a simpler way. Due to its simplicity and worldwide applications, the latest breakthroughs in PSO, as well as their applications in various fields are stated in this chapter. Its significance, algorithm and working mechanism along with the pseudo-code are presented in this chapter. The utility of PSO has been addressed and the flaws in the algorithm have been recognized. The recent advancements and modifications of PSO in terms of its parameters are also discussed. Finally, its hybridization with other illustrious algorithms and applications in multiple disciplines and domains over the last decades are discussed. The motivation for all hybrid optimization techniques is examined for real-world challenges. It has been noticed that PSO can be hybridized with other algorithms and parallel applications.

Cited by 54Open access

Particle swarm optimization

Andries P. Engelbrecht · 2014

tutorial Share on Particle swarm optimization Author: Andries Engelbrecht University of Pretoria, Pretoria, South Africa University of Pretoria, Pretoria, South AfricaView Profile Authors Info & Claims GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary ComputationJuly 2014Pages 381–406https://doi.org/10.1145/2598394.2605342Published:12 July 2014Publication History 4citation433DownloadsMetricsTotal Citations4Total Downloads433Last 12 Months12Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Publisher SiteGet Access

Cited by 83

Particle Swarm Optimization

Luz, Eduardo Fávero Pacheco da, Becceneri, José Carlos, Stephany, Stephan et al. · Biblioteca Digital da Memória Científica do INPE (National Institute for Space Research) · 2023

Cited by 0

Particle Swarm Optimization

Rajesh Kumar Samala · Artificial intelligence · 2023

The procedure is to obtain the best solution for the certain parameters in the given network to satisfy every requirement for design by considering the smallest affordable cost will be considered as an optimization. Optimization traditional approach will have some constraints like, outcome of single-based, local optima convergence, problems in unknown search space. To overcome the above constraints, many research organizations have established various metaheuristics to search optimization solutions for unsolved issues. The main intend to explain the Particle Swarm Optimization algorithm (PSOA) is to explain the stochastic optimization approach basics. Motivation of this Particle Swarm Optimization algorithm (PSOA) is to develop a strong metaheuristic optimization solution which is inspired natural swarm behavior like schooling of birds and fishes. PSOA is a simplified social network simulation. The final intent of this PSOA is a graphical representation and graphical simulate smoothly but undefined bird or fish flock’s directions. Every bird’s vicinity of observability is restricted to some area. Though having many birds permits every bird in the swarm fitness function to be bigger surface concerned. Mathematically every Particle Swarm Optimization algorithm (PSOA) has associated with fitness value, velocity, and position. Memory of maintaining global fitness, best position, and global fitness value.

Cited by 37Open access

Particle swarm optimization

Andries P. Engelbrecht, Christopher W. Cleghorn · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2018

tutorial Share on Particle swarm optimization: a guide to effective, misconception free, real world use Authors: AP Engelbrecht University of Pretoria, South Africa University of Pretoria, South AfricaView Profile , CW Cleghorn University of Pretoria, South Africa University of Pretoria, South AfricaView Profile Authors Info & Claims GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2018 Pages 831–857https://doi.org/10.1145/3205651.3207877Published:06 July 2018Publication History 1citation135DownloadsMetricsTotal Citations1Total Downloads135Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Cited by 53

Particle Swarm Optimization

Gerhard Venter, Jaroslaw Sobieszczanski‐Sobieski · AIAA Journal · 2003

Gerhard Venter (gventer_vrand.conl) *Vanderpla(ds Research and Development, bit.1767 S 8th St'reef. Suite 100, Colorado Springs. CO 80906Jaroslaw Sobieszczanski-Sobieski (j.sobieski:_larc.nasa.gov) *A_4SA Lcmgley Research Ce,_terMS 240, Hampton, I:4 23681-2199The purpose of this paper is to show how the search algorithm, known as par-ticle swarm optimization performs. Here, particle swarm optimization ks appliedto structural design problems, but the method.has a much wider range of possi-ble applications. The paper's new contributions are improvements to the particleswarm optimization algorithm and conclusions and recommendations as to theutility of the algorithm. Results of numerical experiments for both continuousand discrete applications are presented in the paper. The results indicate that theparticle swarm optimization algorithm does locate the constrained minimum de-sign in continuous applications with very good precision, albeit at a much highercomputational cost than that of a typical gradient based optimizer. However, thetrue potential of particle swarm optimization is primarily in applications withdiscrete and/or discontinuous functions and variables. Additionally, particleswarm optimization has the potential of e3_icient computation with very largenumbers of concurrently operating processors.

Cited by 784