Journal ArticleDOI
Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions
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TLDR
The proposed AFPSO utilizes fuzzy set theory to adjust PSO acceleration coefficients adaptively, and is thereby able to improve the accuracy and efficiency of searches, and incorporating this algorithm with quadratic interpolation and crossover operator further enhances the global searching capability.About:
This article is published in Information Sciences.The article was published on 2011-10-01. It has received 95 citations till now. The article focuses on the topics: Particle swarm optimization & Global optimization.read more
Citations
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Journal ArticleDOI
Particle swarm optimization algorithm: an overview
Dongshu Wang,Dapei Tan,Lei Liu +2 more
TL;DR: Its origin and background is introduced and the theory analysis of the PSO is carried out, which analyzes its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithms, multi-objective optimization PSO and its engineering applications.
Journal ArticleDOI
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
TL;DR: This survey presented a comprehensive investigation of PSO, including its modifications, extensions, and applications to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology.
Proceedings Article
A review of population-based meta-heuristic algorithm
TL;DR: Several population-based meta-heuristics in continuous (real) and discrete (binary) search spaces are explained in details and design, main algorithm, advantages and disadvantages of the algorithms are covered.
Journal ArticleDOI
An Adaptive Particle Swarm Optimization With Multiple Adaptive Methods
TL;DR: A parameter control mechanism to adaptively change the parameters and thus improve the robustness of PSO-MAM is proposed and developed that is expected to be more robust than PSO -MAM and compared with state-of-the-art PSO algorithms and evolutionary algorithms.
Journal ArticleDOI
A Supervised Learning and Control Method to Improve Particle Swarm Optimization Algorithms
Wenyong Dong,MengChu Zhou +1 more
TL;DR: An adaptive particle swarm optimization with supervised learning and control (APSO-SLC) for the parameter settings and diversity maintenance of particle Swarm optimization to adaptively choose parameters, while improving its exploration competence.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Journal ArticleDOI
Evolutionary programming made faster
Xin Yao,Yong Liu,Guangming Lin +2 more
TL;DR: A "fast EP" (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator and is proposed and tested empirically, showing that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested.
Journal ArticleDOI
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
TL;DR: The comprehensive learning particle swarm optimizer (CLPSO) is presented, which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity.
Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization
Ponnuthurai Nagaratnam Suganthan,Nikolaus Hansen,Jing Liang,Kalyanmoy Deb,Y. P. Chen,Anne Auger,Santosh Tiwari +6 more
TL;DR: This special session is devoted to the approaches, algorithms and techniques for solving real parameter single objective optimization without making use of the exact equations of the test functions.