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26-07-2020 | Regular Research Paper | Issue 3/2020

Memetic Computing 3/2020

Hybrid many-objective cuckoo search algorithm with Lévy and exponential distributions

Journal:
Memetic Computing > Issue 3/2020
Authors:
Zhihua Cui, Maoqing Zhang, Hui Wang, Xingjuan Cai, Wensheng Zhang, Jinjun Chen
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Abstract

Hybrid many-objective cuckoo search algorithm (HMaOCS) is a newly proposed method for Many-objective optimization problems (MaOPs), and has achieved promising performance. However, Lévy and Gaussian distributions used in global search manner of HMaOCS is originally proposed for optimization problems with one objective, and they are not suitable for MaOPs as illustrated in this paper. To further exploit the potential of HMaOCS, this paper investigates four different probability distributions and their six corresponding combinations. Comparison results illustrate that the combination of Lévy and Exponential distributions is able to greatly improve HMaOCS. On the basis of comparison results and analysis on both DTLZ and WFG test suites with 2, 3, 4, 6, 8 and 10 objectives, it can be concluded that HMaOCS with Lévy and Exponential distributions exhibits better performance compared with most advanced algorithms.

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