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Improvement of particle swarm optimization based on the repetitive search guideline
Author(s) -
Hiraoka Sodo,
Okamoto Takashi,
Aiyoshi Eitaro
Publication year - 2010
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.20964
Subject(s) - guideline , particle swarm optimization , particle (ecology) , metaheuristic , computer science , medicine , artificial intelligence , machine learning , biology , pathology , ecology
Particle Swarm Optimization (PSO), which has attracted a great deal of attention as a global optimization method in recent years, has the drawback that continuous search based on its excellent dynamic characteristics cannot be performed stably until the end of computation due to its very strong tendency to convergence. In this paper, we propose a ARepetitive Search Guidelinewhich differs from the common guidelines in the improved methods which have since been proposed and by which the continuous search in PSO is achieved without losing PSO's excellent dynamic characteristics due to repetitive search in a promising area where the objective function values are expected to be small. We consider four improved methods based on the proposed guidelines, then confirm their effectiveness by application to 100‐variable multipeaked benchmark problems. 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 173(2): 42–54, 2010; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/ eej.20964