An Novel Artificial Immune Systems Multi-objective Optimization Algorithm for 0/1 Knapsack Problems

摘要

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.