Multiobjective optimization using an immunodominance and clonal selection inspired algorithm

作     者：GONG MaoGuo JIAO LiCheng MA WenPing DU HaiFeng 

作者机构：Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China School of Public and Administration Xi'an Jiaotong University Xi'an 710049 China 

出 版 物：《Science in China(Series F)》 (中国科学（F辑英文版）)

年 卷 期：2008年第51卷第8期

页      面：1064-1082页

核心收录：

学科分类：1001[医学-基础医学(可授医学、理学学位)] 100102[医学-免疫学] 10[医学] 

基　　金：the National Natural Science Foundation of China(Grant Nos.60703107 and 60703108) the National High Technology Research and Development Program(863 Program) of China(Grant No.2006AA01Z107) the National Basic Research Program(973 Program) of China(Grant No.2006CB705700) the Program for Cheung Kong Scholars and Innovative Research Team in University(Grant No.IRT0645) 

主　　题：multiobjective optimization immunodominance clonal selection artificial immune systems evolutionaryalgorithms 

摘      要：Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm （IDCMA）. IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals （antibodies） are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the subdominant antibodies, while the cryptic antibodies are redundant and have no function during local search, but they can become subdominant （active） antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performance comparison of IDCMA with MISA, NSGA-Ⅱ, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.