An novel artificial immune systems multi-objective optimization algorithm for 0/1 knapsack problems

Authors:
Wenping Ma;Licheng Jiao;Maoguo Gong;Fang Liu
Affiliations:
Institute of Intelligent Information Processing, Xidian University, Xi’an, China;Institute of Intelligent Information Processing, Xidian University, Xi’an, China;Institute of Intelligent Information Processing, Xidian University, Xi’an, China;School of Computer Science and Technology, Xidian University, Xi’an, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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Abstract

Based on the concept of Immunodominance and Antibody Clonal Selection Theory, This paper proposes a new artificial immune system algorithm, Immune Dominance Clonal Multiobjective Algorithm (IDCMA), for multiobjective 0/1 knapsack problems. IDCMA divides the individual population into three sub-populations and adopts different evolution and selection strategies at them, but the update of each sub-population is not carried out all alone. The performance comparisons among IDCMA, SPEA, HLGA, NPGA, NSGA and VEGA show that IDCMA clearly outperforms the other five MOEAs in terms of solution quality.