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Chicken swarm optimization with an enhanced exploration–exploitation tradeoff and its application

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Abstract

The chicken swarm optimization (CSO) is a novel swarm intelligence algorithm, which mimics the hierarchal order and foraging behavior in the chicken swarm. However, like other population-based algorithms, CSO also suffers from slow convergence and easily falls into local optima, which partly results from the unbalance between exploration and exploitation. To tackle this problem, this paper proposes a chicken swarm optimization with an enhanced exploration–exploitation tradeoff (CSO-EET). To be specific, the search process in CSO-EET is divided into two stages (i.e., exploration and exploitation) according to the swarm diversity. In the exploratory search process, a random solution is employed to find promising solutions. In the exploitative search process, the best solution is used to accelerate convergence. Guided by the swarm diversity, CSO-EET alternates between exploration and exploitation. To evaluate the optimization performance of CSO-EET in both theoretical and practical problems, it is compared with other improved CSO variants and several state-of-the-art algorithms on two groups of widely used benchmark functions (including 102 test functions) and two real-world problems (i.e., circle packing problem and survival risk prediction of esophageal cancer). The experimental results show that CSO-EET is better than or at least comparable to all competitors in most cases.

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Data availability

The date used to support the findings of this study are available from the corresponding author upon request.

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Funding

This research was funded by the Joint Funds of the National Natural Science Foundation of China (No. U1804262), in part by the National Natural Science Foundation of China (No. 61702463), in part by the Science and Technology Foundation of Henan Province (Grant Nos. 222102210277), and in part by the Innovation Incubation Project of Zhengzhou University of Light Industry (Grant No. 2021ZCKJ203).

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

    Yingcong Wang, Chengcheng Sui, Chi Liu, Junwei Sun & Yanfeng Wang

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  1. Yingcong Wang
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  2. Chengcheng Sui
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  3. Chi Liu
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  4. Junwei Sun
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  5. Yanfeng Wang
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All the authors contributed equally to this research paper.

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Correspondence to Yanfeng Wang.

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Wang, Y., Sui, C., Liu, C. et al. Chicken swarm optimization with an enhanced exploration–exploitation tradeoff and its application. Soft Comput 27, 8013–8028 (2023). https://doi.org/10.1007/s00500-023-07990-8

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