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Hybrid multi-objective cuckoo search with dynamical local search

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Abstract

Cuckoo search (CS) is a recently developed meta-heuristic, which has shown good search abilities on many optimization problems. In this paper, we present a hybrid multi-objective CS (HMOCS) for solving multi-objective optimization problems (MOPs). The HMOCS employs the non-dominated sorting procedure and a dynamical local search. The former is helpful to generate Pareto fronts, and the latter focuses on enhance the local search. In order to verify the performance of our approach HMOCS, six well-known benchmark MOPs were used in the experiments. Simulation results show that HMOCS outperforms three other multi-objective algorithms in terms of convergence, spread and distributions.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Nos. 61663028 and 61403271, and is also supported by Natural Science Foundation of Shanxi Province under Grant No. 201601D011045.

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Authors and Affiliations

  1. Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, 030024, Shanxi, China

    Maoqing Zhang, Zhihua Cui & Jinjun Chen

  2. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Hui Wang

  3. University of Technology Sydney, Sydney, NSW, 2007, Australia

    Jinjun Chen

Authors
  1. Maoqing Zhang
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  2. Hui Wang
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  3. Zhihua Cui
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  4. Jinjun Chen
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Correspondence to Zhihua Cui.

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Zhang, M., Wang, H., Cui, Z. et al. Hybrid multi-objective cuckoo search with dynamical local search. Memetic Comp. 10, 199–208 (2018). https://doi.org/10.1007/s12293-017-0237-2

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