摘要
传统多目标进化算法(MOEA)在解决具有复杂Pareto解集的多目标优化问题(CPS_MOP)时存在严重的退化现象.为此,本文提出两种进化模型——基于个体的进化模型和基于种群的进化模型.并在此基础上,设计两类基于拉丁超立方体抽样(LHS)的MOEA(LHS-MOEA).LHS-MOEA采用LHS局部搜索开采目前较优秀的区域,采用进化操作在可行解空间中探测新的搜索区域,从而有效克服退化现象.实验结果表明,LHS-MOEA求解CPS_MOPs的效果较好,比经典算法NSGA-II具有明显的优势.
Two evolutionary models, individual based evolutionary model （IND） and population based evolutionary model （POP） are proposed. Based on these two models, two kinds of multi-objective evolutionary algorithms （LHS） are designed based on Latin hypercube sampling, namely LHS-MOEAs. In LHS-MOEAs, the LHS local search is designed for exploiting promising areas and the evolutionary operator is designed for exploring new searching areas in feasible space. The combination of LHS local search and evolutionary operator in LHS-MOEA can prevent degeneration effectively. Experimental results demonstrate that the proposed LHS-MOEAs performs better and it is more preponderant than the classical NSGA-II in solving CPS_MOPs.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2009年第2期223-233,共11页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.60773047)
湖南省教育厅重点科研项目(No.06A074)资助
