会议专题

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

2010 International Conference on Bio-inspried System and Signal Processing(2010 IEEE生物系统与信号处理国际会议 ICBSSP 2010)

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)