Clonal selection with immune dominance and anergy based multiobjective optimization

Authors:
Licheng Jiao;Maoguo Gong;Ronghua Shang;Haifeng Du;Bin Lu
Affiliations:
Institute of Intelligent Information Processing, Xidian University, Xi'an, P.R. China;Institute of Intelligent Information Processing, Xidian University, Xi'an, P.R. China;Institute of Intelligent Information Processing, Xidian University, Xi'an, P.R. China;Institute of Intelligent Information Processing, Xidian University, Xi'an, P.R. China;Institute of Intelligent Information Processing, Xidian University, Xi'an, P.R. China
Venue:
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Year:
2005

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Abstract

Based on the concept of Immunodominance and Antibody Clonal Selection Theory, we propose a new artificial immune system algorithm, Immune Dominance Clonal Multiobjective Algorithm (IDCMA). The influences of main parameters are analyzed empirically. The simulation comparisons among IDCMA, the Random-Weight Genetic Algorithm and the Strength Pareto Evolutionary Algorithm show that when low-dimensional multiobjective problems are concerned, IDCMA has the best performance in metrics such as Spacing and Coverage of Two Sets.