A hybrid many-objective cuckoo search algorithm
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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.
KeywordsCuckoo search Many-objective optimization problems Non-dominated sorting Reference points
This study is funded by the National Natural Science Foundation of China under Grant Nos. 61806138, U1636220, 61663028, 71771176, 51775385, 61703279 and 71371142, Natural Science Foundation of Shanxi Province under Grant No. 201801D121127, PhD Research Startup Foundation of Taiyuan University of Science and Technology under Grant No. 20182002, the Distinguished Young Talents Plan of Jiang-xi Province under Grant No. 20171BCB23075, the Natural Science Foundation of Jiang-xi Province under Grant No. 20171BAB202035.
Compliance with ethical standards
Conflict of interest
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.
This article does not contain any studies with human participants or animals performed by any of the authors.
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