Solving Constrained Optimization Problems by an Improved Particle Swarm Optimization

Chaoli Sun, Jianchao Zeng, Shu-Chuan Chu, John Roddick, Jeng-Shyang Pan

    Research output: Contribution to conferencePaper

    7 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages124-128
    Number of pages5
    DOIs
    Publication statusPublished - 2011
    Event2nd International Conference on Innovations in Bio-inspired Computing and Applications -
    Duration: 16 Dec 2011 → …

    Conference

    Conference2nd International Conference on Innovations in Bio-inspired Computing and Applications
    Period16/12/11 → …

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  • Cite this

    Sun, C., Zeng, J., Chu, S-C., Roddick, J., & Pan, J-S. (2011). Solving Constrained Optimization Problems by an Improved Particle Swarm Optimization. 124-128. Paper presented at 2nd International Conference on Innovations in Bio-inspired Computing and Applications, . https://doi.org/10.1109/IBICA.2011.35