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A Competitive Co-Evolutionary Approach for the Multi-Objective Evolutionary Algorithms
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DOI：10.1109/ACCESS.2020.2982251.png)
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摘要
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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 & x2019;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 co-evolutionary algorithms with a robust performance.
Keyword:
Dual-population
convergence
diversity
co-evolution
competitive
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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