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Competitive search algorithm: a new method for stochastic optimization

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Abstract

A novel approach of swarm intelligence(SI) optimization, namely Competitive Search Algorithm(CSA), is proposed in this paper based on some social activities in human life, such as all-around sports competitions and talent variety shows. Firstly, the mathematical model and the algorithm framework are introduced and the working principle is explained in detail. Then the computational complexity and the parameter sensitivity in the proposed algorithm are analyzed. Moreover, it is compared and tested with the eleven algorithms commonly used such as the algorithms of Archimedes optimization, the particle swarm in 15 test functions and CEC’14 test functions. The results show that the proposed algorithm has obvious advantages in the search accuracy, the convergence speed and the stability. Finally, the algorithm CSA is applied to the maximum power point tracking(MPPT) in the photovoltaic system and the reactive power optimization of active distribution network. Therefore, the effectiveness of the proposed algorithm is verified.

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Authors and Affiliations

  1. Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang, China

    Yanchun Xu, Haiquan Liu, Shasha Xie & Lei Xi

  2. Department of Electrical & Computer Engineering, Texas A&M University, TX, College Station, USA

    Mi Lu

Authors
  1. Yanchun Xu
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  2. Haiquan Liu
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  3. Shasha Xie
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  4. Lei Xi
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  5. Mi Lu
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Correspondence to Haiquan Liu.

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Xu, Y., Liu, H., Xie, S. et al. Competitive search algorithm: a new method for stochastic optimization. Appl Intell 52, 12131–12154 (2022). https://doi.org/10.1007/s10489-021-03133-4

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  • DOI: https://doi.org/10.1007/s10489-021-03133-4

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