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A hybrid many-objective cuckoo search algorithm

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Abstract

Cuckoo search (CS) is an excellent population-based algorithm and has shown promising performance in dealing with single- and multi-objective optimization problems. However, for many-objective optimization problems (MaOPs), CS cannot be directly employed. So far, few paper have been reported to use CS to solve MaOPs. In this paper, we try to propose a hybrid many-objective cuckoo search (HMaOCS) for MaOPs. In HMaOCS, the standard CS is firstly modified to effectively deal with MaOPs. Then, non-dominated sorting and the strategy of reference points are employed to ensure the convergence and diversity. In order to verify the performance of HMaOCS, DTLZ and WFG benchmark sets are utilized in the experiments. Experimental results show that HMaOCS can achieve promising performance compared with five other well-known many-objective optimization algorithms.

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Author information

Correspondence to Maoqing Zhang or Xingjuan Cai.

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Author Zhihua Cui declares that he has no conflict of interest. Author Maoqing Zhang declares that he has no conflict of interest. Author Hui Wang declares that he has no conflict of interest. Author Xingjuan Cai declares that she has no conflict of interest. Author Wensheng Zhang declares that he has no conflict of interest.

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Cite this article

Cui, Z., Zhang, M., Wang, H. et al. A hybrid many-objective cuckoo search algorithm. Soft Comput 23, 10681–10697 (2019). https://doi.org/10.1007/s00500-019-04004-4

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Keywords

  • Cuckoo search
  • Many-objective optimization problems
  • Non-dominated sorting
  • Reference points