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A memetic algorithm for the quadratic multiple container packing problem

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Abstract

In this paper, we propose a new memetic algorithm for the quadratic multiple container packing problem. The proposed memetic algorithm is based on the adaptive link adjustment evolutionary algorithm (ALA-EA) and it incorporates heuristic fitness improvement schemes into the ALA-EA. In addition, we propose a new powerful initialization method for the QMCPP.

The proposed algorithm is compared to the previous approaches. We report that it can find much improved final solutions at most benchmark instances. Moreover, even the best solutions of the first generation show better than the previous known best solutions at most of instances especially with density d=0.75.

In addition, we compare the proposed memetic algorithm to the NetKey based evolutionary algorithm for analyzing features of the ALA-EA.

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References

  1. Ahuja RK, Orlin JB, Tivari A (1995) A greedy genetic algorithm for the quadratic assignment problem. Working paper 3826-95, Sloan School of Management, MIT

  2. Bean JC (1994) Genetic algorithms and random keys for sequencing and optimization. ORSA J Comput 6(2):154–160

    MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  4. Granmo O-C, Oommen BJ (2010) Optimal sampling for estimation with constrained resources using a learning automaton-based solution for the nonlinear fractional knapsack problem. App Intell. doi:10.2007/s10489-010-0228-1

  5. Gruau F, Whitley D (1993) Adding learning to the cellular development of neural networks evolution and the Baldwin effect. Evol Comput 1(3):213–233

    Article  Google Scholar 

  6. Hiley A, Julstrom BA (2006) The quadratic multiple knapsack problem and three heuristic approaches to it. In: Procs. of the genetic and evolutionary computation conference, vol 1, pp 547–552

  7. Julstrom BA (1999) Comparing darwinian, baldwinian, and lamarckian search in a genetic algorithm for the 4-cycle problem. Late breaking paper at the 1997 genetic and evolutionary computation conference

  8. Kimbrough SO, Koehler GJ, Lu M, Wood DH (2008) On a Feasible Infeasible Two-Population (FI-2Pop) genetic algorithm for constrained optimization: distance tracing and no free lunch. Eur J Oper Res 190:310–327

    Article  MATH  MathSciNet  Google Scholar 

  9. Kruskal JB (1956) On the shortest spanning tree of a graph and the travelling salesman problem. Proc Am Math Soc 7(1):48–50

    Article  MATH  MathSciNet  Google Scholar 

  10. Lee S, Soak SM, Kim K, Park H, Jeon M (2007) Statistical properties analysis of real world tournament selection in GAs. In: Applied intelligence, vol 28, pp 195–205

  11. Li Y, Zeng X (2008) Multi-population co-genetic algorithm with double chain-like agents structure for parallel global numerical optimization. Appl Intell

  12. Likas A, Papageorgiou G, Stafylopatis A (1997) A connectionist approach for solving large constraint satisfaction problems. Appl Intell 7:215–225

    Article  Google Scholar 

  13. Lim A, Rodrgues B, Yang Y (2005) 3-D Container packing problem. Appl Intell 22:125–134

    Article  MATH  Google Scholar 

  14. Misevičius A (2003) Genetic algorithm hybridized with ruin and recreate procedure: application to the quadratic assignment problem. Knowl-Based Syst 16:261–268

    Article  Google Scholar 

  15. Misevičius A (2004) An improved hybrid genetic algorithm: new results for the quadratic assignment problem. Knowl-Based Syst 17:65–73

    Article  Google Scholar 

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

    Chapter  Google Scholar 

  17. Rothlauf F, Goldberg D, Heinzl A (2002) Network random keys—a tree network representation scheme for genetic and evolutionary algorithms. Evol Comput 10(1):75–97

    Article  Google Scholar 

  18. Sarac T, Sipahioglu A (2007) A genetic algorithm for the quadratic multiple knapsack problem. In: BVAI2007. LNCS, vol 4729. Springer, Berlin, pp 490–498

    Google Scholar 

  19. Schindler B, Rothlauf F, Pesch H (2002) Evolution strategies, network random keys, and the one-max tree problem. In: Evoworkshops. Springer, Berlin, pp 143–152

    Google Scholar 

  20. Schumacher C Vose MD Whitely LD The no free lunch and description length. In: Proceedings of genetic and evolutionary computation conference (GECCO-2001), pp 565–570

  21. Singh A, Baghel AS (2007) A new grouping algorithm for the quadratic multiple knapsack problem. In: EvoCOP 2007. LNCS, vol 4446. Springer, Berlin, pp 210–218

    Google Scholar 

  22. Singh A, Gupta AK (2006) A hybrid heuristic for the maximum clique problem. J Heuristic 12:5–22

    Article  MATH  Google Scholar 

  23. 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 

  24. Soak SM, Corne D, Ahn BH (2004) A new encoding for the degree constrained minimum spanning tree problem. In: KES 2004, LNAI, vol. 3213. Springer, Berlin, pp 952–958

    Google Scholar 

  25. 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 

  26. Soak SM, Lee SW, Jeon MG (2008) The improved adaptive link adjustment evolutionary algorithm for the multiple container packing problem. Appl Intell. doi:10.1007/s10489-008-0155-6

  27. Soak SM, Lee SW, Yeo GT, Jeon MG An effective evolutionary algorithm for the multiple container packing problem. In: The second international conference on bio-inspired computing: theories and applications (BIC-TA 2007)

  28. Vazquez M, Whitley LD A hybrid genetic algorithm for the quadratic assignment problem. In: Genetic and evolutionary computation conference (GECCO-2000), pp 169–178

  29. Whitley D, Gordon VS, Mathias KE (1994) Lamarckian evolution, the baldwin effect and function optimization. In: Parallel problem solving from nature III, pp 6–15

  30. Wolpert DH, Maredady WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

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Authors and Affiliations

  1. Information Examination Team, Korean Intellectual Property Office, Gov. Complex Daejeon Bldg. 4, 920, Dunsandong, Seo-gu, Daejeon, Republic of Korea

    Sang-Moon Soak

  2. Dept. of Information and Communication, Mokwon University, Mokwon Gil 21, Seo-gu, Daejeon, 302-318, Republic of Korea

    Sang-Wook Lee

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  1. Sang-Moon Soak
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  2. Sang-Wook Lee
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Correspondence to Sang-Wook Lee.

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Soak, SM., Lee, SW. A memetic algorithm for the quadratic multiple container packing problem. Appl Intell 36, 119–135 (2012). https://doi.org/10.1007/s10489-010-0248-x

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