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The improved adaptive link adjustment evolutionary algorithm for the multiple container packing problem

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Abstract

In this paper, we propose a new version of Adaptive Link Adjustment Evolutionary Algorithm (ALA-EA) for the network optimization problems, and apply it to the multiple container packing problem (MCPP). Because the proposed algorithm uses a different encoding method from that of the original ALA-EA, we also need different decoding methods for the new algorithm. In addition, to improve the performance of the proposed algorithm, we incorporate heuristic local improvement approaches into it. To verify the effectiveness of the proposed algorithm we compare it with the existing evolutionary approaches for several instances, which are known to be extremely difficult to them. Computational tests show that the algorithm is superior to the existing evolutionary approaches and the original ALA-EA in both of the solution quality and the computational time. Moreover, the performance seems to be not affected by an instance property.

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References

  1. Christofides N, Mingozzi A, Toth P (1979) The 0–1 knapsack problem. In: Christofides N, Mingozzi A, Toth P, Sandi C (eds) Combinatorial optimization. Wiley, New York, pp 339–369

    Google Scholar 

  2. Chu PC, Beasley JE (1998) A genetic algorithm for the multidimensional knapsack problem. J Heuristics 4:63–86

    Article  MATH  Google Scholar 

  3. Lee S, Soak SM, Kim K, Park H, Jeon M (2007) Statistical properties analysis of real world tournament selection in GAs. Appl Intell 28:195–205

    Article  Google Scholar 

  4. Martello S, Toth P (1980) Solution of the zero-one multiple knapsack problem. Eur J Oper Res 4:276–283

    MATH  MathSciNet  Google Scholar 

  5. Pisinger D (1999) An exact algorithm for large multiple knapsack problems. Eur J Oper Res 114:528–541

    Article  MATH  Google Scholar 

  6. Raidl GR (1999) A weight-coded genetic algorithm for the multiple container packing problem. In: Proceedings of the 1999 ACM symposium on applied computing, pp 291–296

  7. Raidl GR (1999) The multiple container packing problem: A genetic algorithm approach with weighted codings. ACM Appl Comput Rev 7(2):22–31

    Article  Google Scholar 

  8. Raidl GR, Kodydek G (1998) Genetic algorithms for the multiple container packing problem. In: Proceedings of the 5th international conference on parallel problem solving from nature (PPSN V). LNCS, vol 1498. Springer, Berlin, pp 875–884

    Chapter  Google Scholar 

  9. Soak SM (2007) Adaptive link adjustment’ applied to the fixed charge transportation problem. IEICE Trans Fundam Electron Commun Comput Sci E90-A(12):2863–2876

    Article  Google Scholar 

  10. Soak SM, Corne D, Ahn BH (2005) A new evolutionary algorithm for spanning-tree based communication network design. IEICE Trans Commun E88-B(10):4090–4093

    Article  Google Scholar 

  11. Soak SM, Corne D, Ahn BH (2006) The edge-window-decoder representation for tree-based problems. IEEE Trans Evol Comput 10(2):124–144

    Article  Google Scholar 

  12. Soak SM, Lee SW, Yeo GT, Jeon MG (2008) An effective evolutionary algorithm for the multiple container packing problem. Prog Nat Sci 18(3):337–344

    Article  Google Scholar 

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Correspondence to Moongu Jeon.

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Soak, SM., Lee, SW. & Jeon, M. The improved adaptive link adjustment evolutionary algorithm for the multiple container packing problem. Appl Intell 33, 144–158 (2010). https://doi.org/10.1007/s10489-008-0155-6

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  • DOI: https://doi.org/10.1007/s10489-008-0155-6

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