Many-Objective Evolutionary Algorithm based on Dominance Degree

For many-objective optimization problems, the comparability of non-dominated solutions is always an essential and fundamental issue. Due to the inefficiency of Pareto dominance for many-objective optimization problems, various improved dominance relations have been proposed to enhance the evolutiona...

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Bibliographic Details
Published in:Applied soft computing Vol. 113; p. 107869
Main Authors: Zhang, Maoqing, Wang, Lei, Guo, Weian, Li, Wuzhao, Pang, Junwei, Min, Jun, Liu, Hanwei, Wu, Qidi
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2021
Subjects:
Dominance degree
Dominance relations
Evolutionary algorithm
Pareto dominance
Dominance relations
Dominance degree
Evolutionary algorithm
Pareto dominance
ISSN:1568-4946, 1872-9681
Online Access:Get full text
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Summary:For many-objective optimization problems, the comparability of non-dominated solutions is always an essential and fundamental issue. Due to the inefficiency of Pareto dominance for many-objective optimization problems, various improved dominance relations have been proposed to enhance the evolutionary pressure. However, these variants have one thing in common that they treat each solution in a static manner, and the relations between any two solutions are just defined as a kind of static spatial adjacencies, resulting in the unquantifiable comparability. Different from them, this paper proposes a dominance degree metric, which treats solutions as different stages of a dynamic motion process. The dynamic motion process represents the continuous changes of the degree of one solution from Pareto dominating others to being Pareto dominated by others. Based on the dominance degree, this paper proposes a Many-Objective Evolutionary Algorithm based on Dominance Degree, in which the mating selection and environmental update strategies are redesigned accordingly. The proposed method is comprehensively tested with several state-of-the-art optimizers on two popular test suites and practical multi-point distance minimization problems. Experimental results demonstrate its effectiveness and superiority over other optimizers in terms of the convergence, diversity and spread. •It is observed that regular methods for dominance relations are described in a static manner.•Dominance degree is proposed for reflecting the dynamic relationships of solutions.•Many-objective Evolutionary Algorithm based on Dominance Degree is proposed and tested.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2021.107869