Multi-objective Optimization Immune Algorithm Using Clustering
In this paper, a Multi-objective Optimization Immune Algorithm Using Clustering (CMOIA) is proposed. The mutation operator based on affinity definition can make the generated antibodies develop into a much better group. It combines local search ability of evolutionary algorithm by using crossover and genetic mutation operators to operate on the immune mutated antibodies. Then a clustering based clonal selection operator is used to maintain a balance between exploration and exploitation. Four general multiobjective optimization problems are selected to test algorithm performance according to the widely used four performance indicator. It was shown that the Pareto fronts obtained by CMOIA were better convergence and diversity than the ones from the other four classical multi-objective optimization evolutionary algorithms. The experiment results show the highly competitive in terms of the originality and robustness of the proposed algorithm.
Multi-objective Optimization Artificial Immune Systems Immune Optimization Clonal Selection
Sun Fang Chen Yunfang Wu Weimin
College of Computer Science Nanjing University of Posts and Telecommunications Nanjing, China
国际会议
厦门
英文
9-13
2010-10-26(万方平台首次上网日期,不代表论文的发表时间)