面向多目标优化的适应度共享免疫克隆算法  

Immune clonal algorithm with fitness sharing for multi-objective optimization

作　　者：林浒 彭勇[1,2] 

机构地区：[1]中国科学院沈阳计算技术研究所,辽宁沈阳110171 [2]中国科学院研究生院,北京100049

出　　处：《控制理论与应用》2011年第2期206-214,共9页Control Theory & Applications

基　　金：中国科学院知识创新工程重要方向性资助项目(KGCX2–YW–119)

摘　　要：多目标优化的目标在于使得解集能够快速的逼近真实Pareto前沿.针对解的分布性问题,以免疫克隆算法为框架,引入适应度共享策略,提出了一种新的具有良好分布性保持的多目标优化进化算法;算法建立外部群体以保存非支配解,以Pareto占优和共享适应度作为外部群体更新与激活抗体选择的双重标准.为了增强算法对决策空间的开发能力,引入佳点搜索方法,在决策空间生成具有均匀散布特征的佳点集.通过数值实验,与经典的多种多目标进化算法比较,新算法得到的解集在收敛性和分布性方面均具有明显的改善.The purpose of the multi-objective optimization is to quickly find out the Pareto optimal solutions which converge to the ideal Pareto front with a good performance in diversity.Based on the immune clonal theory,this paper introduces the fitness sharing strategy;and then a new multi-objective optimization evolutionary algorithm with good performance in diversity is proposed for maintaining the diversity of solutions.The proposed algorithm employs an external archive to preserve the non-dominated solutions.The principle which includes sharing fitness and Pareto domination is used to update the external archive mentioned above and select the active antibodies for generating offspring.Moreover,for enhancing the search ability in the decision space,this paper introduces the good-point-searching approach which can generate the good-point set with uniform distribution.The proposed algorithm is tested on several multi-objective optimization problems and compared with many classical methods;much better performances in both the convergence and diversity of obtained solutions are observed.

关 键 词：多目标优化 免疫克隆算法 适应度共享 佳点集 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]