Abstract
Particle Swarm Optimization (PSO), which has attracted a great deal of attention as a global optimization method in recent years, has a drawback in that its continuous search based on its excellent dynamic characteristics can not be executed stably until the end of computation due to its much strong convergence trend. In this paper, we propose "Repetitive Search Guideline" which differs from a common guideline in the improved methods which have ever been proposed and by which the continuous search of PSO is achieved without lack of PSO's excellent dynamic characteristics due to the repetitive search in a promise area where objective function value is expected to be small. We consider four improved methods based on the proposed guideline, and then, their effectiveness are confirmed through applications to 100 variables multi-peaked benchmark problems.
| Original language | English |
|---|---|
| Journal | IEEJ Transactions on Electronics, Information and Systems |
| Volume | 128 |
| Issue number | 7 |
| Publication status | Published - 2008 |
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Keywords
- Global optimization
- Meta-heuristics
- Particle swarm optimization
ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
Improvement of particle swarm optimization based on the repetitive search guideline. / Hiraoka, Sodo; Okamoto, Takashi; Aiyoshi, Eitaro.
In: IEEJ Transactions on Electronics, Information and Systems, Vol. 128, No. 7, 2008.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Improvement of particle swarm optimization based on the repetitive search guideline
AU - Hiraoka, Sodo
AU - Okamoto, Takashi
AU - Aiyoshi, Eitaro
PY - 2008
Y1 - 2008
N2 - Particle Swarm Optimization (PSO), which has attracted a great deal of attention as a global optimization method in recent years, has a drawback in that its continuous search based on its excellent dynamic characteristics can not be executed stably until the end of computation due to its much strong convergence trend. In this paper, we propose "Repetitive Search Guideline" which differs from a common guideline in the improved methods which have ever been proposed and by which the continuous search of PSO is achieved without lack of PSO's excellent dynamic characteristics due to the repetitive search in a promise area where objective function value is expected to be small. We consider four improved methods based on the proposed guideline, and then, their effectiveness are confirmed through applications to 100 variables multi-peaked benchmark problems.
AB - Particle Swarm Optimization (PSO), which has attracted a great deal of attention as a global optimization method in recent years, has a drawback in that its continuous search based on its excellent dynamic characteristics can not be executed stably until the end of computation due to its much strong convergence trend. In this paper, we propose "Repetitive Search Guideline" which differs from a common guideline in the improved methods which have ever been proposed and by which the continuous search of PSO is achieved without lack of PSO's excellent dynamic characteristics due to the repetitive search in a promise area where objective function value is expected to be small. We consider four improved methods based on the proposed guideline, and then, their effectiveness are confirmed through applications to 100 variables multi-peaked benchmark problems.
KW - Global optimization
KW - Meta-heuristics
KW - Particle swarm optimization
UR - http://www.scopus.com/inward/record.url?scp=70349205069&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70349205069&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:70349205069
VL - 128
JO - IEEJ Transactions on Electronics, Information and Systems
JF - IEEJ Transactions on Electronics, Information and Systems
SN - 0385-4221
IS - 7
ER -