J4 ›› 2014, Vol. 41 ›› Issue (6): 89-94.doi: 10.3969/j.issn.1001-2400.2014.06.015

• 研究论文 • 上一篇    下一篇



  1. (1. 西安电子科技大学 计算机学院,陕西 西安  710071;
    2. 武警工程大学 军事基础教育学院,陕西 西安  710038)
  • 收稿日期:2013-08-08 出版日期:2014-12-20 发布日期:2015-01-19
  • 通讯作者: 代才
  • 作者简介:代才(1984-), 男, 西安电子科技大学博士研究生, E-mail: daicai8403@126.com.
  • 基金资助:

    国家自然科学基金资助项目(61272119 和61203372)

New ranking method for many-objective problems

DAI Cai1;WANG Yuping1;HE Xiaoguang2   

  1. (1. School of Computer Science and Technology, Xidian Univ., Xi'an  710071, China;
    2. Military Basic Institute of Education, Engineering University of CAPF, Xi'an  710038, China)
  • Received:2013-08-08 Online:2014-12-20 Published:2015-01-19
  • Contact: DAI Cai



关键词: 高维多目标优化, 进化算法, 排序, 帕累托最优


A new ranking method is proposed to solve many-objective optimization problems. It can be used to generates many approximate optimal objective vectors to increase the size of population, so that the individuals can be efficiently sorted by using non-dominance sorting. An ideal Pareto Front is first constructed, and then the ideal Pareto Front is divided into a number of grids. For each individual, it is uniquely assigned to a grid, and then some nodes of the grid are used to determine whether the individual is a non-dominance solution. Experimental results show that, even for 50-objective optimization problems, the convergence measurements of solutions obtained by the ranking method are smaller than 1. Meanwhile, compared with two state-of-the-art relaxed forms of Pareto dominance, the experimental results show that this ranking method can simultaneously maintain the diversity of solutions and have good convergence.

Key words: many-objective optimization, evolutionary algorithms, ranking, Pareto optimisation


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