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Immune Clonal MO Algorithm for 0/1 Knapsack Problems

  • Ronghua Shang
  • Wenping Ma
  • Wei Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

Abstract

In this paper, we introduce a new multiobjective optimization (MO) algorithm to solve 0/1 knapsack problems using the immune clonal principle. This algorithm is termed Immune Clonal MO Algorithm (ICMOA). In ICMOA, the antibody population is split into the population of the nondominated antibodies and that of the dominated antibodied. Meanwhile, the nondominated antibodies are allowed to survive and to clone. A metric of Coverage of Two Sets is adopted for the problems. This quantitative metric is used for testing the convergence to the Pareto-optimal front. Simulation results on the 0/1 knapsack problems show that ICMOA, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared with SPEA, NSGA, NPGA and VEGA.

Keywords

Pareto Front Multiobjective Optimization Knapsack Problem Artificial Immune System Multiobjective Evolutionary Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ronghua Shang
    • 1
  • Wenping Ma
    • 1
  • Wei Zhang
    • 1
  1. 1.Institute of Intelligent Information ProcessingXidian UniversityXi’anP.R. China

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