Enhanced particle swarm optimizer incorporating a weighted particle

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Title:	Enhanced particle swarm optimizer incorporating a weighted particle
Authors:	王文俊;Li, Nai-Jen;Wang, Wen-June;James Hsu, Chen-Chien;Chang, Wei;Chou, Hao-Gong;Chang, Jun-Wei
Contributors:	資訊電機學院電機工程學系
Keywords:	Applied sciences;Artificial intelligence;Benchmarking;Computer science;control theory;systems;Computer simulation;Connectionism. Neural networks;Control system synthesis;Control theory. Systems;Convergence;Design engineering;Evolutionary;Exact sciences and technology;Fundamental areas of phenomenology (including applications);Inverted pendulum system;Modelling and identification;Neural network;Neural networks;Optimization;Particle swarm optimization (PSO);Physics;PID controller design;Searching;Solid dynamics (ballistics, collision, multibody system, stabilization...);Solid mechanics;Swarm intelligence;Weighted particle
Date:	2014-01-26
Issue Date:	2026-04-23 13:58:41 (UTC+8)
Publisher:	Elsevier;Amsterdam: Elsevier B.V
Abstract:	摘要： This study proposes an enhanced particle swarm optimizer incorporating a weighted particle (EPSOWP) to improve the evolutionary performance for a set of benchmark functions. In conventional particle swarm optimizer (PSO), there are two principal forces to guide the moving direction of each particle. However, if the current particle lies too close to either the personal best particle or the global best particle, the velocity is mainly updated by only one term. As a result, search step becomes smaller and the optimization of the swarm is likely to be trapped into a local optimum. To address this problem, we define a weighted particle for incorporation into the particle swarm optimization. Because the weighted particle has a better opportunity getting closer to the optimal solution than the global best particle during the evolution, the EPSOWP is capable of guiding the swarm to a better direction to search the optimal solution. Simulation results show the effectiveness of the EPSOWP, which outperforms various evolutionary algorithms on a selected set of benchmark functions. Furthermore, the proposed EPSOWP is applied to controller design and parameter identification for an inverted pendulum system as well as parameter learning of neural network for function approximation to show its viability to solve practical design problems. 出版者： Amsterdam: Elsevier B.V 出版日期： 2014-01-26 出處： Neurocomputing (Amsterdam), 2014-01, Vol.124, p.218-227 版權： 2013 Elsevier B.V. 版權： 2015 INIST-CNRS 識別號： ISSN: 0925-2312 識別號： EISSN: 1872-8286 識別號： DOI: 10.1016/j.neucom.2013.07.005
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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