A Clonal Selection Adaptive Local Search Operator for multi-objective optimization evolutionary algorithm

作     者：Yong Li 1 , Yu Wang 2 , Yuxian Zhang 1 , Yuejun An 1 1. School of Electrical Engineering, Shenyang University of Technology, Shenyang 1108702. Department of Automatic Control, Shenyang Aerospace University, Shenyang 110136 

会议名称：《第24届中国控制与决策会议》

会议日期：2012年

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 081202[工学-计算机软件与理论] 

基　　金：supported by the science and technology research project of education department of Liaoning(201134123) science and technology planproject of Liaoning(2010220011) science and technology planproject of Shenyang(F11-190-7-00) the national natural science foundation of China(61102124) 

关 键 词：Clonal selection. Adaptive local search operator. Multi objective optimization evolutionary algorithm. 

摘      要：A clonal selection adaptive local search operator for multi-objective optimization evolutionary algorithm is proposed in order to enhance the search capability and expedite convergence speed of the multi-objective evolutionary algorithms. A crossover based adaptive local search algorithm including a method to change the clonal scale of different individuals adaptively according to their position in the whole population is proposed. Test functions with two objectives and three objectives are selected to confirm the performance of the operator. Results show that the clonal selection adaptive local operator makes multi-objective optimization evolutionary algorithm has better performance in convergence and distribution.