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Solving multi-objective optimization problem using cuckoo search algorithm based on decomposition

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Abstract

In recent years, cuckoo search (CS) algorithm has been successfully applied in single-objective optimization problems. In addition, decomposition-based multi-objective evolutionary algorithms (MOEA/D) have high performance for multi-objective optimization problems (MOPs). Inspired by this, a new decomposition-based multi-objective CS algorithm is proposed in this paper. Two reproduction operators with different characteristics derived from the CS algorithm are constructed and they compose an operator pool. Then, a bandit-based adaptive operator selection method is used to determine the application of different operators. An angle-based selection strategy that achieves a better balance between convergence and diversity is adopted to preserve diversity. Compared with other improved strategies designed for MOEA/D on two suits of test instances, the proposed algorithm was demonstrated to be effective and competitive for MOPs.

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Authors and Affiliations

  1. No.1, Haifu Lane, Guanghua Road, Qinhuai District, Nanjing City, Jiangsu Prov, China

    Liang Chen, Wenyan Gan, Hongwei Li & Kai Cheng

  2. No.1155, Yanshan Road, Yuhui District, Bengbu City, Anhui Prov, China

    Liang Chen & Zili Zhang

  3. No. 1 Hongjing Avenue, Jiangning Science Park, Nanjing City, Jiangsu Prov, China

    Darong Pan & Li Chen

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  1. Liang Chen
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  2. Wenyan Gan
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Chen, L., Gan, W., Li, H. et al. Solving multi-objective optimization problem using cuckoo search algorithm based on decomposition. Appl Intell 51, 143–160 (2021). https://doi.org/10.1007/s10489-020-01816-y

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