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Chicken Swarm Optimization Based on Elite Opposition-Based Learning

Author

Listed:
  • Chiwen Qu
  • Shi’an Zhao
  • Yanming Fu
  • Wei He

Abstract

Chicken swarm optimization is a new intelligent bionic algorithm, simulating the chicken swarm searching for food in nature. Basic algorithm is likely to fall into a local optimum and has a slow convergence rate. Aiming at these deficiencies, an improved chicken swarm optimization algorithm based on elite opposition-based learning is proposed. In cock swarm, random search based on adaptive distribution is adopted to replace that based on Gaussian distribution so as to balance the global exploitation ability and local development ability of the algorithm. In hen swarm, elite opposition-based learning is introduced to promote the population diversity. Dimension-by-dimension greedy search mode is used to do local search for individual of optimal chicken swarm in order to improve optimization precision. According to the test results of 18 standard test functions and 2 engineering structure optimization problems, this algorithm has better effect on optimization precision and speed compared with basic chicken algorithm and other intelligent optimization algorithms.

Suggested Citation

  • Chiwen Qu & Shi’an Zhao & Yanming Fu & Wei He, 2017. "Chicken Swarm Optimization Based on Elite Opposition-Based Learning," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-20, March.
    • Handle: RePEc:hin:jnlmpe:2734362
    DOI: 10.1155/2017/2734362
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    Cited by:

    1. Turgut, Oguz Emrah & Turgut, Mert Sinan, 2023. "Local search enhanced Aquila optimization algorithm ameliorated with an ensemble of Wavelet mutation strategies for complex optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 206(C), pages 302-374.
    2. Yan, Zheping & Zhang, Jinzhong & Tang, Jialing, 2021. "Path planning for autonomous underwater vehicle based on an enhanced water wave optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 192-241.
    3. Deb, Sanchari & Gao, Xiao-Zhi & Tammi, Kari & Kalita, Karuna & Mahanta, Pinakeswar, 2021. "A novel chicken swarm and teaching learning based algorithm for electric vehicle charging station placement problem," Energy, Elsevier, vol. 220(C).

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