Improvement of particle swarm optimization based on the repetitive search guideline

Sodo Hiraoka, Takashi Okamoto, Eitaro Aiyoshi

Research output: Contribution to journalArticle

Abstract

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 "Repetitive Search Guideline" which 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.

Original languageEnglish
Pages (from-to)42-54
Number of pages13
JournalElectrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
Volume173
Issue number2
DOIs
Publication statusPublished - 2010 Nov 15

Fingerprint

Particle swarm optimization (PSO)
Global optimization

Keywords

  • Global optimization
  • Meta-heuristics
  • Particle swarm optimization

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology

Cite this

Improvement of particle swarm optimization based on the repetitive search guideline. / Hiraoka, Sodo; Okamoto, Takashi; Aiyoshi, Eitaro.

In: Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi), Vol. 173, No. 2, 15.11.2010, p. 42-54.

Research output: Contribution to journalArticle

@article{2f563cd8ef904d37ab1d284324d8752f,
title = "Improvement of particle swarm optimization based on the repetitive search guideline",
abstract = "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 {"}Repetitive Search Guideline{"} which 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.",
keywords = "Global optimization, Meta-heuristics, Particle swarm optimization",
author = "Sodo Hiraoka and Takashi Okamoto and Eitaro Aiyoshi",
year = "2010",
month = "11",
day = "15",
doi = "10.1002/eej.20964",
language = "English",
volume = "173",
pages = "42--54",
journal = "Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)",
issn = "0424-7760",
publisher = "John Wiley and Sons Inc.",
number = "2",

}

TY - JOUR

T1 - Improvement of particle swarm optimization based on the repetitive search guideline

AU - Hiraoka, Sodo

AU - Okamoto, Takashi

AU - Aiyoshi, Eitaro

PY - 2010/11/15

Y1 - 2010/11/15

N2 - 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 "Repetitive Search Guideline" which 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.

AB - 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 "Repetitive Search Guideline" which 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.

KW - Global optimization

KW - Meta-heuristics

KW - Particle swarm optimization

UR - http://www.scopus.com/inward/record.url?scp=77956125828&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77956125828&partnerID=8YFLogxK

U2 - 10.1002/eej.20964

DO - 10.1002/eej.20964

M3 - Article

AN - SCOPUS:77956125828

VL - 173

SP - 42

EP - 54

JO - Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)

JF - Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)

SN - 0424-7760

IS - 2

ER -