An effective hybrid EDA-based algorithm for solving multidimensional knapsack problem

  • Authors:

  • Ling Wang;Sheng-yao Wang;Ye Xu

  • Affiliations:

  • Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China;Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China;Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

In this paper, an effective hybrid algorithm based on estimation of distribution algorithm (EDA) is proposed to solve the multidimensional knapsack problem (MKP). With the framework of EDA, the probability model is built with the superior population and the new individuals are generated based on probability model. In addition, an updating mechanism of the probability model is proposed and a mechanism for initializing the probability model based on the specific knowledge of the MKP is also proposed to improve the convergence speed. Meanwhile, an adaptive local search is proposed to enhance the exploitation ability. Furthermore, the influences of parameters are investigated based on Taguchi method of design of experiment and the importance of repair operator is also studied via simulation testing and comparisons. Finally, numerical simulation is carried out based on the benchmark instances, and the comparisons with some existing algorithms demonstrate the effectiveness of the proposed algorithm.