J4 ›› 2011, Vol. 38 ›› Issue (1): 47-53.doi: 10.3969/j.issn.1001-2400.2011.01.008

• 研究论文 • 上一篇    下一篇



  1. (西安电子科技大学 智能感知与图像理解教育部重点实验室,陕西 西安   710071)
  • 收稿日期:2010-01-07 出版日期:2011-02-20 发布日期:2011-04-08
  • 通讯作者: 刘若辰
  • 作者简介:刘若辰(1974-),女,副教授,博士,E-mail: ruochenenliu@yahoo.com.cn.
  • 基金资助:

    国家自然科学基金资助项目(60803098, 60703108);国家教育部博士点基金资助项目(20070701022);中国博士后科学基金资助项目(20080431228, 20090451369);陕西省自然科学基金资助项目(2009JQ8015)

New differential evolution constrained optimization algorithm

LIU Ruochen;JIAO Licheng;LEI Qifeng;FANG Lingfen   

  1. (Ministry of Education Key Lab. of Intelligent Perception and Image Understanding, Xidian Univ., Xi'an  710071, China)
  • Received:2010-01-07 Online:2011-02-20 Published:2011-04-08
  • Contact: LIU Ruochen



关键词: 差分进化算法, 约束优化, 多目标优化


Most existing differential evolution algorithms for the Constrained Optimization Problem(COP) use the penalty function method to handle constrains, which depends strongly on the penalty parameter. So, this paper transforms the COP into two-objective multi-objective optimization by taking constraints as an objective function. Based on the concept of Pareto, the grades of individuals in population are prescribed so as to determine their selection probability in the process of “survival of the fittest”. In addition, when the algorithm gets into a local optimum, an infeasible solution replacing mechanism is also given to improve the search capability. The results of the 13 Standard tests show that compared to the Evolutionary Algorithm based on Homomorphous Maps (EAHM), Constraint Handling Differential Evolution (CHDE), Evolutionary Strategies based on Stochastic Ranking (ESSR) and Artificial Immune Response Constrained Evolutionary Strategy (AIRCES), the proposed algorithm has certain advantages in convergence speed and solution accuracy.

Key words: differential evolution algorithm, constrained optimization, multi-objective optimization