Immune clonal algorithm with fitness sharing for multi-objective optimization
面向多目标优化的适应度共享免疫克隆算法

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.