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Chaos embedded particle swarm optimization algorithms

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  • Alatas, Bilal
  • Akin, Erhan
  • Ozer, A. Bedri

Abstract

This paper proposes new particle swarm optimization (PSO) methods that use chaotic maps for parameter adaptation. This has been done by using of chaotic number generators each time a random number is needed by the classical PSO algorithm. Twelve chaos-embedded PSO methods have been proposed and eight chaotic maps have been analyzed in the benchmark functions. It has been detected that coupling emergent results in different areas, like those of PSO and complex dynamics, can improve the quality of results in some optimization problems. It has been also shown that, some of the proposed methods have somewhat increased the solution quality, that is in some cases they improved the global searching capability by escaping the local solutions.

Suggested Citation

  • Alatas, Bilal & Akin, Erhan & Ozer, A. Bedri, 2009. "Chaos embedded particle swarm optimization algorithms," Chaos, Solitons & Fractals, Elsevier, vol. 40(4), pages 1715-1734.
    • Handle: RePEc:eee:chsofr:v:40:y:2009:i:4:p:1715-1734
    DOI: 10.1016/j.chaos.2007.09.063
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    1. Zuo, X.Q. & Fan, Y.S., 2006. "A chaos search immune algorithm with its application to neuro-fuzzy controller design," Chaos, Solitons & Fractals, Elsevier, vol. 30(1), pages 94-109.
    2. M. Senthil Arumugam & M. V. C. Rao, 2006. "On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems," Discrete Dynamics in Nature and Society, Hindawi, vol. 2006, pages 1-17, June.
    3. Gao, Haojiang & Zhang, Yisheng & Liang, Shuyun & Li, Dequn, 2006. "A new chaotic algorithm for image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 29(2), pages 393-399.
    4. Liu, Bo & Wang, Ling & Jin, Yi-Hui & Tang, Fang & Huang, De-Xian, 2005. "Improved particle swarm optimization combined with chaos," Chaos, Solitons & Fractals, Elsevier, vol. 25(5), pages 1261-1271.
    5. Jiao, Bin & Lian, Zhigang & Gu, Xingsheng, 2008. "A dynamic inertia weight particle swarm optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 37(3), pages 698-705.
    6. Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2009. "A novel chaotic particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch," Chaos, Solitons & Fractals, Elsevier, vol. 39(2), pages 510-518.
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    Cited by:

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