Combine LHS with MOEA to Optimize Complex Pareto Set MOPs

Authors:
Jinhua Zheng;Biao Luo;Miqing Li;Jing Li
Affiliations:
Research Center of Evolutionary Computation and Intelligent System, Xiangtan University, Hunan, China 411105;Research Center of Evolutionary Computation and Intelligent System, Xiangtan University, Hunan, China 411105;Research Center of Evolutionary Computation and Intelligent System, Xiangtan University, Hunan, China 411105;Research Center of Evolutionary Computation and Intelligent System, Xiangtan University, Hunan, China 411105
Venue:
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Year:
2008

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Abstract

The Pareto set (PS) of real multi-objective optimization problems (MOPs) are often unknown and complex, so, it is significant for multi-objective evolutionary algorithms (MOEAs) to solve complex PS MOPs (CPS_MOPs namely). In this paper, we combined Latin hypercube sampling (LHS) with MOEA, proposed a LHS based MOEA (LHS-MOEA). We suggested two kinds of LHS-MOEA, in which LHS local search and evolutionary operator are combined to handle CPS_MOPs. Through some experiments, the results demonstrate that LHS-MOEA performs much better than the traditional prevalent MOEA -- NSGA-II in solving CPS_MOPs.