Novel Binary Biogeography-Based Optimization Algorithm for the Knapsack Problem

  • BiBingyan Zhao
  • Changshou Deng
  • Yanling Yang
  • Hu Peng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)


Mathematical models of biogeography inspired the development of the biogeography-based optimization algorithm. In this article we propose a binary version of biogeography-based optimization (BBO) for the Knapsack Problem. Two new mutation operators are proposed to extend the biogeography-based optimization algorithm to binary optimization problems. We also demonstrate the performance of the resulting new binary Biogeography-based optimization algorithm in solving four Knapsack problems and compare it with that of the standard Genetic Algorithm. The simulation results show that our new method is effective and efficient for the Knapsack problem.


Knapsack Problem Biogeography-based optimization Migration operator mutation operator 


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  1. 1.
    Masako, F., Takeo, Y.: An exact algorithm for the Knapsack sharing problem with common items. European Journal of Operational Research 171(2), 693–707 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Didier, E.B., Moussa, E.: Load balancing methods and parallel dynamic programming algorithm using dominance technique applied to the 0-1 Knapsack Problem. Journal of Parallel and Distributed Computing 65(1), 74–84 (2005)CrossRefGoogle Scholar
  3. 3.
    Stephen, C.H., Leung, Z.D.F., Zhou, C.L., Wu, T.: A hybrid simulated annealing metaheuristic algorithm for the two-dimensional Knapsack packing problem. Computers & Operations Research 39(1), 64–73 (2010)Google Scholar
  4. 4.
    Kong, M., Peng, T., Kao, Y.C.: A new ant colony optimization algorithm for the multidimensional Knapsack Problem. Computers & Operations Research 35(8), 2672–2683 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Maria, J.A., Marla, A., Motg, A.: A multiobjective Tchebycheff based genetic algorithm for the multidimensional Knapsack Problem. Computers & Operations Research 34(11), 3458–3470 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Zhang, L., Zhang, B.: Good point set based genetic algorithm. Chinese Journal of Computers 24(9), 917–922 (2001)MathSciNetGoogle Scholar
  7. 7.
    Shen, X.J., Wang, W.W., Zheng, P.J.: Modified particle swarm optimization for 0/1 Knapsack problem. Computer Engineering 32(18), 23–24, 38 (2006)Google Scholar
  8. 8.
    Gao, T., Wang, M.G.: The research for the reductive dimension and replacive variable algorithm of special restrict ILP. Systems Engineering Theory Methodology Applications 11(2), 125–130 (2002)Google Scholar
  9. 9.
    Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)CrossRefGoogle Scholar
  10. 10.
    Simon, D., Mehmet, E., Dawei, D.: Population Distributions in Biogeography-based Optimization algorithms with Elitism. In: SMC, pp. 1–6 (2009)Google Scholar
  11. 11.
    Simon, D., Mehmet, E., Dawei, D., Rick, R.: Markov Models for Biogeography-Based Optimization. IEEE Transactions on Systems, Man, and Cybernetics—PART B: Cybernetics 41(1), 299–306 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • BiBingyan Zhao
    • 1
  • Changshou Deng
    • 2
  • Yanling Yang
    • 2
  • Hu Peng
    • 2
  1. 1.School of BusinessJiujiang UniversityJiujiangChina
  2. 2.School of Information Science and TechnologyJiujiang UniversityJiujiangChina

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