基于新的网格存优策略的多目标归档算法  

EMO archive algorithm using new grid-based elitist-reserving strategy

作　　者：谢炯亮[1] 郑金华[1] 

机构地区：[1]湘潭大学信息工程学院,湖南湘潭411105

出　　处：《计算机工程与应用》2009年第2期55-60,130,共7页Computer Engineering and Applications

基　　金：国家自然科学基金No.60773047;国家高技术研究发展计划(863)No.2001AA114060;教育部留学回国人员科研启动基金No.教外司留[2005]546号;湖南省自然科学基金No.05JJ30125;湖南省教育厅重点科研项目(No.06A074)~~

摘　　要：网格方法被多个进化算法用来保持解集的分布性。基于ε支配概念的ε-MOEA本质上也是基于网格策略的。虽然ε-MOEA通常情况下都能在算法性能的各方面之间取得较为合理的折衷,但是由于其存在固有缺陷,很多时候表现出不容忽视的问题——当PFtrue对某一维的变化率在该维不同区域的差异较大时,解集中边界个体或代表性个体丢失——严重影响解集的分布性。针对这一问题,定义了一种新的δ支配概念和虚拟"最优点"的概念,提出了一种新的网格存优策略,并将之应用于更新进化多目标归档算法的归档集。实验结果显示,基于新的存优策略的进化多目标归档算法(δ-MOEA)具有良好的性能,尤其在分布性方面比NSGA2和ε-MOEA好得多。Grid-based measure is commonly used to maintain diversity in many MOEAs.The ε-MOEA,which is based on the ε- dominance concept,is essentially based on grid-strategy.Though Often gaining an appropriate tradeoff between the aspects of the performance,the ε-MOEA has its inherent vice and behaves unacceptably sometimes.That is,when the slope to one dimension of the PFtrue changes a lot along it,the algorithm loses many extreme or representative individuals,which has a severely influence on the diversity of the solution set.In order to solve this problem,a new δ-dominance concept and suppositional optimum point concept is defined,then a new grid-based elitist-reserving strategy is proposed,finally it is applied in an EMO archive algorithm（δ-MOEA）.The experimental results illustrate δ-MOEA＇s good performance,which is much better especially at the diversity than NSGA2 and ε-MOEA.

关 键 词：网格 归档集 ε支配 δ支配 虚拟“最优点” 网格存优策略 δ-MOEA 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]