The Cone epsilon-Dominance: An Approach for Evolutionary Multiobjective Optimization

• URL: http://arxiv.org/abs/2008.04224v1
• Date: Tue, 14 Jul 2020 14:13:13 GMT
• Title: The Cone epsilon-Dominance: An Approach for Evolutionary Multiobjective Optimization
• Authors: Lucas S. Batista, Felipe Campelo, Frederico G. Guimar\~aes and Jaime A. Ram\'irez
• Abstract summary: cone epsilon-dominance approach to improve convergence and diversity in evolutionary algorithms. Sixteen well-known benchmark problems are considered in the experimental section. Results suggest that the cone-eps-MOEA is capable of presenting an efficient and balanced performance over all the performance metrics considered.
• Score: 1.0323063834827413
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: We propose the cone epsilon-dominance approach to improve convergence and diversity in multiobjective evolutionary algorithms (MOEAs). A cone-eps-MOEA is presented and compared with MOEAs based on the standard Pareto relation (NSGA-II, NSGA-II*, SPEA2, and a clustered NSGA-II) and on the epsilon-dominance (eps-MOEA). The comparison is performed both in terms of computational complexity and on four performance indicators selected to quantify the quality of the final results obtained by each algorithm: the convergence, diversity, hypervolume, and coverage of many sets metrics. Sixteen well-known benchmark problems are considered in the experimental section, including the ZDT and the DTLZ families. To evaluate the possible differences amongst the algorithms, a carefully designed experiment is performed for the four performance metrics. The results obtained suggest that the cone-eps-MOEA is capable of presenting an efficient and balanced performance over all the performance metrics considered. These results strongly support the conclusion that the cone-eps-MOEA is a competitive approach for obtaining an efficient balance between convergence and diversity to the Pareto front, and as such represents a useful tool for the solution of multiobjective optimization problems.

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