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Adaptive particle swarm optimization using information about global best
Author(s) -
Yamaguchi Teruyoshi,
Iwasaki Nobuhiro,
Yasuda Keiichiro
Publication year - 2007
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.20487
Subject(s) - particle swarm optimization , multi swarm optimization , mathematical optimization , global optimization , computer science , adaptive strategies , metaheuristic , derivative free optimization , nonlinear system , swarm behaviour , optimization problem , mathematics , physics , archaeology , quantum mechanics , history
The Particle Swarm Optimization method is one of the most powerful optimization methods available for solving global optimization problems. However, knowledge of adaptive strategies for tuning the parameters of the method for application to large‐scale nonlinear nonconvex optimization problems is as yet limited. This paper describes an adaptive strategy for tuning the parameters of the PSO method based on some numerical analysis of the behavior of PSO. The proposed adaptive tuning strategy is based on self‐tuning of the parameters of PSO, which utilizes the information about the frequency of an updated global best of a swarm. The feasibility and advantages of the proposed adaptive PSO algorithm are demonstrated through some numerical simulations using three different typical global optimization test problems. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 159(4): 38– 46, 2007; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20487