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Hybrid chaotic ant swarm optimization

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  • Li, Yuying
  • Wen, Qiaoyan
  • Li, Lixiang
  • Peng, Haipeng

Abstract

Chaotic ant swarm optimization (CASO) is a powerful chaos search algorithm that is used to find the global optimum solution in search space. However, the CASO algorithm has some disadvantages, such as lower solution precision and longer computational time, when solving complex optimization problems. To resolve these problems, an improved CASO, called hybrid chaotic swarm optimization (HCASO), is proposed in this paper. The new algorithm introduces preselection operator and discrete recombination operator into the CASO; meanwhile it replaces the best position found by own and its neighbors’ ants with the best position found by preselection operator and discrete recombination operator in evolution equation. Through testing five benchmark functions with large dimensionality, the experimental results show the new method enhances the solution accuracy and stability greatly, as well as reduces the computational time and computer memory significantly when compared to the CASO. In addition, we observe the results can become better with swarm size increasing from the sensitivity study to swarm size. And we gain some relations between problem dimensions and swam size according to scalability study.

Suggested Citation

  • Li, Yuying & Wen, Qiaoyan & Li, Lixiang & Peng, Haipeng, 2009. "Hybrid chaotic ant swarm optimization," Chaos, Solitons & Fractals, Elsevier, vol. 42(2), pages 880-889.
    • Handle: RePEc:eee:chsofr:v:42:y:2009:i:2:p:880-889
    DOI: 10.1016/j.chaos.2009.02.020
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    References listed on IDEAS

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    1. Li, Lixiang & Yang, Yixian & Peng, Haipeng & Wang, Xiangdong, 2006. "Parameters identification of chaotic systems via chaotic ant swarm," Chaos, Solitons & Fractals, Elsevier, vol. 28(5), pages 1204-1211.
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    Cited by:

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    2. Hong, Wei-Chiang, 2010. "Application of chaotic ant swarm optimization in electric load forecasting," Energy Policy, Elsevier, vol. 38(10), pages 5830-5839, October.
    3. Sinha, Sunanda & Chandel, S.S., 2015. "Review of recent trends in optimization techniques for solar photovoltaic–wind based hybrid energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 755-769.
    4. Li, Chaoshun & Zhou, Jianzhong & Xiao, Jian & Xiao, Han, 2012. "Parameters identification of chaotic system by chaotic gravitational search algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 45(4), pages 539-547.

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