An Improved NSGA-III Procedure for Evolutionary Many-Objective Optimization

摘要

Many-objective (four or more objectives) optimization problems pose a great challenge to the classical Pareto-dominance based multi-objective evolutionary algorithms (MOEAs), such as NSGA-II and SPEA2. This is mainly due to the fact that the selection pressure based on Pareto-dominance degrades severely with the number of objectives increasing. Very recently, a reference-point based NSGA-II, referred as NSGA-III, is suggested to deal with many-objective problems, where the maintenance of diversity among population members is aided by supplying and adaptively updating a number of well-spread reference points. However, NSGA-III still relies on Pareto-dominance to push the population towards Pareto front (PF), leaving room for the improvement of its convergence ability. In this paper, an improved NSGA-III procedure, called 牟-NSGA-III, is proposed, aiming to better tradeoff the convergence and diversity in many-objective optimization. In 牟-NSGA-III, the non-dominated sorting scheme based on the proposed 牟-dominance is employed to rank solutions in the environmental selection phase, which ensures both convergence and diversity. Computational experiments have shown that 牟-NSGA-III is significantly better than the original NSGA-III and MOEA/D on most instances no matter in convergence and overall performance.

出版物
Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation
袁源
博士

2010年进组,2015年获得博士学位。

徐华
徐华
长聘副教授, Expert Systems with Application 副主编,博士生导师
王勃
硕士

2012年进组,2015年获得硕士学位。

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