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A competitive co-evolutionary approach for the multi-objective evolutionary algorithms

Vu, VT, Bui, TL and Nguyen, TT (2020) A competitive co-evolutionary approach for the multi-objective evolutionary algorithms. IEEE Access. ISSN 2169-3536

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In multi-objective evolutionary algorithms (MOEAs), convergence and diversity are two basic issues and keeping a balance between them plays a vital role. There are several studies that have attempted to address this problem, but this is still an open challenge. It is thus the purpose of this research to develop a dual-population competitive co-evolutionary approach to improving the balance between convergence and diversity. We utilize two populations to solve separate tasks. The first population uses Pareto-based ranking scheme to achieve better convergence, and the second one tries to guarantee population diversity via the use of a decomposition-based method. Next, by operating a competitive mechanism to combine the two populations, we create a new one with a view to having both characteristics (i.e. convergence and diversity). The proposed method’s performance is measured by the renowned benchmarks of multi-objective optimization problems (MOPs) using the hypervolume (HV) and the inverted generational distance (IGD) metrics. Experimental results show that the proposed method outperforms cutting-edge coevolutionary algorithms with a robust performance.

Item Type: Article
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date Deposited: 19 Mar 2020 09:10
Last Modified: 04 Sep 2021 07:39
DOI or ID number: 10.1109/ACCESS.2020.2982251
URI: https://researchonline.ljmu.ac.uk/id/eprint/12539
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