基于ε-支配的多目标进化算法及自适应ε调整策略  

The ε-Dominance Based Multi-Objective Evolutionary Algorithm and an Adaptive ε Strategy

作　　者：刘鎏[1] 李敏强[1] 林丹[2] 

机构地区：[1]天津大学系统工程研究所,天津300072 [2]天津大学理学院应用数学系,天津300072

出　　处：《计算机学报》2008年第7期1063-1072,共10页Chinese Journal of Computers

基　　金：国家自然科学基金(70571057,70171002);“新世纪优秀人才支持计划”(NCET-05-0253)资助

摘　　要：提出了一类新的基于ε-支配关系的多目标进化算法.该算法采用配对比较选择和稳态替换策略,提高了算法的收敛速度,降低了计算时间.首先,在保持种群分布性上,采用了一种新的基于ε-支配关系的精英保留策略,避免了传统修剪策略所引起的Pareto前沿面的退化.其次,根据不同ε取值分析了算法收敛性,提出了一种自适应ε调整策略.最后,通过5个常用的双目标测试函数的计算,验证了包括该自适应调整策略的多目标进化算法在求解质量上显著强于NSGAII,SPEA2和ε-MOEA等主流多目标进化算法.A novel multi-objective evolutionary algorithm, called ε-dominance multi-objective evolutionary algorithm（EDMOEA）, is proposed in this paper. In the EDMOEA, pair-comparison selective and steady-state replacement are used to replace the conventional Pareto-ranking strategy, which could effectively improve the convergence rate of the algorithm and reduce the computation time. The main component of the new algorithm is the truncating method in archive population. Based on ε-dominance relationship, it maintains the diversity of the population and prevents the degradation of the Pareto front which often occurs in the conventional truncating strategies. Future more, a new adaptive ε setting method is incorporated into EDMOEA. Finally, five binary-objective functions are used to test the performance of the EDMOEA, the Adaptive- EDMOEA（AEDMOEA）,and conventional algorithms such as NSGAII, SPEA2, and ε-MOEA. Experimental results demonstrate that the AEDMOEA and EDMOEA outperform other algorithms on these test functions.

关 键 词：多目标优化 ε-支配 进化算法 ε自适应调整 精英保留策略 稳态策略 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]