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Memetic Computing

, Volume 10, Issue 2, pp 199–208 | Cite as

Hybrid multi-objective cuckoo search with dynamical local search

  • Maoqing Zhang
  • Hui Wang
  • Zhihua CuiEmail author
  • Jinjun Chen
Regular Research Paper

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.

Keywords

Cuckoo search (CS) Multi-objective cuckoo search Dynamical local search Multi-objective optimization 

Notes

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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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Maoqing Zhang
    • 1
  • Hui Wang
    • 2
  • Zhihua Cui
    • 1
    Email author
  • Jinjun Chen
    • 1
    • 3
  1. 1.Complex System and Computational Intelligence LaboratoryTaiyuan University of Science and TechnologyTaiyuanChina
  2. 2.School of Information EngineeringNanchang Institute of TechnologyNanchangChina
  3. 3.University of Technology SydneySydneyAustralia

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