A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization

Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most existing dominance relations show poor performance in balancing them, thus easily leading to a set of solutions concentrating on a small region of the Pareto fronts. In this paper, a novel dominance...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 23; no. 2; pp. 331 - 345
Main Authors: Tian, Ye, Cheng, Ran, Zhang, Xingyi, Su, Yansen, Jin, Yaochu
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Balancing
Convergence
diversity
Dominance
evolutionary algorithm
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
many-objective optimization
Multiple objective analysis
Optimization
Pareto dominance
Pareto optimization
Production scheduling
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most existing dominance relations show poor performance in balancing them, thus easily leading to a set of solutions concentrating on a small region of the Pareto fronts. In this paper, a novel dominance relation is proposed to better balance convergence and diversity for evolutionary many-objective optimization. In the proposed dominance relation, an adaptive niching technique is developed based on the angles between the candidate solutions, where only the best converged candidate solution is identified to be nondominated in each niche. Experimental results demonstrate that the proposed dominance relation outperforms existing dominance relations in balancing convergence and diversity. A modified NSGA-II is suggested based on the proposed dominance relation, which shows competitiveness against the state-of-the-art algorithms in solving many-objective optimization problems. The effectiveness of the proposed dominance relation is also verified on several other existing multi- and many-objective evolutionary algorithms.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2018.2866854