A Fast and Elitist Grid Selection Evolutionary Algorithm for Multi-objective Optimization: GSEA

摘要：

Multi-objective optimization is an important and challenging topic in the field of industrial design and scientific research. Evolutionary algorithm is a population-based meta-heuristic technique to effectively solve Multi-objective Optimization Problem (MOP). In this paper, a novel EA is proposed, which applied the construction strategy of the elitist population based on spacial grid. In this strategy, firstly, a fast obtaining Pareto set approach with less computation cost is employed; then we filter Pareto set with the grid with the fixed side length to keep the diversity of solutions. Experimental results on test problems show that the GSEA we proposed improves time performance significantly, and is able to find solutions with good diversity and being nearer the true Paretooptimal front compared to the known NSGA-II, SPEA2 and ε·MOEA.

作者: Yufang Qin Junzhong Ji Yang Song Yamin Wang Chunnian Liu

作者单位: College of Computer Science and Technology, Beijing University of Technology Beijing Municipal Key L College of Computer Science and Technology, Beijing University of Technology Beijing Municipal Key L

会议类型: 国际会议

会议名称: 2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)

会议地点: 上海

会议语种:英文

页码: 1254-1258

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）