Premium
A basic study of adaptive particle swarm optimization
Author(s) -
Ide Azuma,
Yasuda Keiichiro
Publication year - 2005
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.20077
Subject(s) - particle swarm optimization , mathematical optimization , adaptability , multi swarm optimization , robustness (evolution) , metaheuristic , computer science , heuristics , optimization problem , derivative free optimization , mathematics , ecology , biochemistry , chemistry , gene , biology
Abstract This paper points out that meta‐heuristics should have not only robustness and adaptability to problems with different structure but also adjustability of parameters included in their algorithms. Particle swarm optimization (PSO), whose concept began as a simulation of a simplified social milieu, is known as one of the most powerful optimization methods for solving nonconvex continuous optimization problems. Then, in order to improve adjustability, a new parameter is introduced into PSO on the basis of the proximate optimality principle (POP). In this paper, we propose adaptive PSO and the effectiveness and the feasibility of the proposed approach are demonstrated on simulations using some typical nonconvex optimization problems. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 151(3): 41–49, 2005; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20077