Advertisement

A Differentiating Evolutionary Computation Approach for the Multidimensional Knapsack Problem

  • Meysam Mohagheghi Fard
  • Yoon-Teck Bau
  • Chien-Le Goh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)

Abstract

In this paper, the DEC (Differentiating Evolutionary Computation) algorithm is presented for solving a zero-one multidimensional knapsack problem. It has three new improvements. They are the use of a chromosome bank for elitism, the use of the superior clan and the inferior clan to improve exploitation and exploration, and the use of genetic modification to enable faster convergence. The experimental results have shown that the DEC algorithm is better than a greedy algorithm and a generic genetic algorithm. It can find solutions very close to those found by the algorithm proposed by Chu & Beasley.

Keywords

multidimensional knapsack problem evolutionary computation genetic algorithm DEC algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Petersen, C.C.: Computational Experience with Variants of the Balas Algorithm Applied to the Selection of R&D Projects. Management Science, 736 (1967)Google Scholar
  2. 2.
    Chu, P., Beasley, J.: A Genetic Algorithm for the Multidimensional Knapsack Problem. Journal of Heuristics 4(1), 63–86 (1998)zbMATHCrossRefGoogle Scholar
  3. 3.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Swiss Federal Institute of Technology (ETH) Zurich, Computer Engineering and Networks Laboratory (TIK), Department of Electrical Engineering (2001)Google Scholar
  4. 4.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  5. 5.
    Shao, Y., Xu, H., Yin, W.: Solve Zero-One Knapsack Problem by Greedy Genetic Algorithm. In: IEEE 2009 International Workshop on Intelligent Systems and Applications (ISA), pp. 1–4 (2009)Google Scholar
  6. 6.
    Lin, C.: A Heuristic Genetic Algorithm Based on Schema Replacement for 0-1 Knapsack Problem. In: 2010 Fourth International Conference on Genetic and Evolutionary Computing (ICGEC), Shenzhen, China, pp. 301–304 (2010)Google Scholar
  7. 7.
    Fréville, A.: The multidimensional 0–1 knapsack problem: An overview. European Journal of Operational Research 155(1), 1–21 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Akçay, Y., Li, H., Xu, S.: Greedy algorithm for the general multidimensional knapsack problem. Annals of Operations Research 150(1), 17–29 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Beasley, J.: OR-Library, Multidimensional knapsack problem, http://people.brunel.ac.uk/~mastjjb/jeb/orlib/mknapinfo.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Meysam Mohagheghi Fard
    • 1
  • Yoon-Teck Bau
    • 1
  • Chien-Le Goh
    • 1
  1. 1.Faculty of Computing and InformaticsMultimedia University, Persiaran MultimediaCyberjayaMalaysia

Personalised recommendations