计算机工程与应用 ›› 2009, Vol. 45 ›› Issue (29): 60-63.DOI: 10.3778/j.issn.1002-8331.2009.29.017

• 研究、探讨 • 上一篇    下一篇

一种改进的非支配排序多目标遗传算法

陈 静,伍 军,郑金华   

  1. 湘潭大学 信息工程学院,湖南 湘潭 411105
  • 收稿日期:2008-06-06 修回日期:2008-09-04 出版日期:2009-10-11 发布日期:2009-10-11
  • 通讯作者: 陈 静

Improved non-dominated sorting genetic algorithm for multi-objective optimization

CHEN Jing,WU Jun,ZHENG Jin-hua   

  1. Institute of Information Engineering,Xiangtan University,Xiangtan,Hunan 411105,China
  • Received:2008-06-06 Revised:2008-09-04 Online:2009-10-11 Published:2009-10-11
  • Contact: CHEN Jing

摘要: 多目标进化算法的研究目标主要是使算法快速收敛,并且广泛而均匀分布于问题的非劣最优域。在NSGA-II算法的基础上,提出了一种新的构造种群的策略——按照聚集距离选取部分非支配个体,并选取部分较好的支配个体形成下一代种群。该策略与原算法相结合后的算法(NSGA-II+IMP)与原NSGA-II进行比较,结果表明新算法较好地改善了分布性和收敛性。

关键词: 多目标进化算法, 多目标优化问题, 种群维护, 聚集距离, 分布性, 保持策略

Abstract: The main goal for research on MOEAs is to make the algorithms converge rapidly,and gain solutions that are widely and uniformly scattered in the non-dominated feasible areas of the problems.This paper,which is on the basis of NSGA2,propo-
ses a new strategy for generating new population,that is not only selecting a certain proportion of non-dominated individuals according to the crowding distance,but also choosing some other dominated but potential individuals to form the next generation.The new strategy-combined algorithm(NSGA-II+IMP) is compared with the original NSGA2,and the result shows that the new one can better improve the diversity and the convergence of the solution set.

Key words: multi-objective evolutionary algorithm, multi-objective optimal problem, population maintenance, crowding distance, diversity, maintenance strategy

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