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  • 标题:A Hybrid Genetic Algorithm for Constrained Optimization Problems
  • 本地全文:下载
  • 作者:Liu, Da-lian ; Chen, Xiao-hua ; Du, Jin-ling
  • 期刊名称:Journal of Computers
  • 印刷版ISSN:1796-203X
  • 出版年度:2013
  • 卷号:8
  • 期号:2
  • 页码:272-278
  • DOI:10.4304/jcp.8.2.272-278
  • 语种:English
  • 出版社:Academy Publisher
  • 摘要:Abstract —Genetic algorithm (GA) is a powerful method to solve constrained optimization problems (COPs). In this paper, a new fitness function based hybrid genetic optimization algorithm (NFFHGA) for COPs is proposed, in which a new crossover operator based on Union Design is presented, and inspired by the smooth function technique, a new fitness function is designed to automatically search for potential solutions. Furthermore, in order to make the fitness function work well, a special technique which keeps a certain number of feasible solutions is also used. Experiments on 6 benchmark problems are performed and the compared results with the best known solutions reported in literature show that NFFHGA can not only quickly converge to the optimal or near-optimal solutions, but also have a high performance.
  • 关键词:Constrained optimization;genetic algorithm;fitness function;Uniform Design.
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