Journal of South China University of Technology (Natural Science Edition) ›› 2011, Vol. 39 ›› Issue (2): 130-135.doi: 10.3969/j.issn.1000-565X.2011.02.022

• Electronics, Communication & Automation Technology • Previous Articles     Next Articles

Multi-Objective Evolutionary Algorithm Based on Direction Selection Search

Li Xue-qiang1  Hao Zhi-feng2  Huang Han3   

  1. 1. South China university of technology, computer science and engineering college, guangdong guangzhou 510006; 2.Computer college of guangdong university of technology, guangdong guangzhou 510090; 3. South China university of technology of software institute, guangdong guangzhou 510006
  • Received:2010-05-21 Online:2011-02-25 Published:2011-01-02
  • Contact: 李学强(1983-)，男，博士生，主要从事多目标优化研究 E-mail:lxqchn@163.com
  • About author:李学强(1983-)，男，博士生，主要从事多目标优化研究
  • Supported by:

    国家自然科学基金资助项目(60873078，61003066，61070033)；高等学校博士学科点专项科研基金资助项目(20090172120035)；广东省自然科学基金资助项目(9251009001000005)；广东省科技计划项目(2010B0504(0)011，2010B080701070，2008B080701005)；华南理工大学中央高校基本科研业务费专项资金资助项目(2009ZM0052)

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Abstract:

When the Pareto solution space is piecewise continuous,the multi-objective optimization algorithm based on the min-max strategy and the pre-set weights may result in the search of the best value by each generation in non-optimal area.In order to solve this problem,a novel judge mechanism is proposed to estimate whether there exists an optimal solution in the Pareto solution space according to the solution set selected in different directions,and the search area is thus adjusted.Moreover,in order to avoid local convergence and prematurity of the proposed algorithm,a direction selection search method based on the external storage mechanism is put forward.Finally,the proposed algorithm is used to test some common multi-objective test functions and some difficult multi-objective test functions in CEC2009 Race.The results indicate that the algorithm is effective.

Key words: evolutionary algorithms, multi-objective optimization, min-max strategy