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A Hybrid Adaptive Multi-objective Memetic Algorithm for 0/1 Knapsack Problem

  • XiuPing Guo
  • ZhiMing Wu
  • GenKe Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)

Abstract

A hybrid adaptive memetic algorithm for a multi-objective combinatorial optimization problem is proposed in this paper. Different solution fitness evaluation methods are hybridized to achieve global exploitation and exploration. At each generation, a wide diversified set of weights are used to search across all regions in objective space, and each weighted linear utility function is optimized with a simulated annealing. For a broader exploration, a grid-based technique is employed to discover the missing nondominated regions on existing tradeoff surface, and a Pareto-based local perturbation is used to reproduce additional good individuals trying to fill up the discontinuous areas. For better stability and convergence of the algorithm, the procedure is made dynamic and adaptive to online optimization conditions based upon a function of improvement ratio. Experiment results show the effectiveness of the proposed method on multi-objective 0/1 knapsack problems.

Keywords

Local Search Simulated Annealing Pareto Front Multiobjective Optimization Knapsack Problem 
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 2005

Authors and Affiliations

  • XiuPing Guo
    • 1
  • ZhiMing Wu
    • 1
  • GenKe Yang
    • 1
  1. 1.Department of AutomationShanghai Jiaotong University, ShanghaiShanghaiP.R. China

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