Solving Constrained Optimization Problems by an Improved Particle Swarm Optimization

Constrained optimization problems compose a large part of real-world applications. More and more attentions have gradually been paid to solve this kind of problems. An improved particle swarm optimization (IPSO) algorithm based on feasibility rules is presented in this paper to solve constrained opt...

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Bibliographic Details
Published in2011 Second International Conference on Innovations in Bio-inspired Computing and Applications pp. 124 - 128
Main Authors Chaoli Sun, Jianchao Zeng, Shuchuan Chu, Roddick, J. F., Jengshyang Pan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2011
Subjects
Algorithm design and analysis
constrained optimi-zation problems
Convergence
Educational institutions
feasibility rules
History
Optimization
Particle swarm optimization
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Summary:Constrained optimization problems compose a large part of real-world applications. More and more attentions have gradually been paid to solve this kind of problems. An improved particle swarm optimization (IPSO) algorithm based on feasibility rules is presented in this paper to solve constrained optimization problems. The average velocity of the swarm and the best history position in the particle's neighborhood are introduced as two turbulence factors, which are considered to influence the fly directions of particles, into the algorithm so as not to converge prematurely. The performance of IPSO algorithm is tested on 13 well-known benchmark functions. The experimental results show that the proposed IPSO algorithm is simple, effective and highly competitive.
ISBN:1457712199
9781457712197
DOI:10.1109/IBICA.2011.35