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Incorporating domain-specific heuristics in a particle swarm optimization approach to the quadratic assignment problem

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Abstract

We propose two variations on particle swarm optimization (PSO): the use of a heuristic function as an additional biasing term in PSO solution construction; and the use of a local search step in the PSO algorithm. We apply these variations to the hierarchical PSO model and evaluate them on the quadratic assignment problem (QAP). We compare the performance of our method to diversified-restart robust tabu search (DivTS), one of the leading approaches at present for the QAP. Our experimental results, using instances from the QAPLIB instance library, indicate that our approach performs competitively with DivTS.

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References

  1. Abdelbar A, Abdelshahid S (2003) Swarm optimization with instinct-driven particles. In: Proceedings of the 2003 IEEE congress on evolutionary computation, (CEC ’03), vol 2. pp 777–782

  2. Abdelbar A, Abdelshahid S (2004) Instinct-based PSO with local search applied to satisfiability. In: Proceedings of the 2004 IEEE international joint conference on neural networks, (IJCNN ’04), vol 3. pp 2291–2295

  3. Ahuja RK, Jha KC, Orlin JB, Sharma D (2007) Very large-scale neighborhood search for the quadratic assignment problem. INFORMS J Comput 19(4):646–657

    Article  MATH  MathSciNet  Google Scholar 

  4. Assad AA, Xu W (1985) On lower bounds for a class of quadratic 0, 1 programs. Oper Res Lett 4(4):175–180

    Article  MATH  MathSciNet  Google Scholar 

  5. Bashiri M, Karimi H (2012) Effective heuristics and meta-heuristics for the quadratic assignment problem with tuned parameters and analytical comparisons. J Ind Eng Int 8(1):1–9

    Article  Google Scholar 

  6. Berretta R, Moscato P (1999) The number partitioning problem: An open challenge for evolutionary computation? In: Corne D, Dorigo M, Glover F, Dasgupta D, Moscato P, Poli R, Price KV (eds) New ideas in optimization. McGraw-Hill, Maidenhead, pp 261–278

    Google Scholar 

  7. Buriol L, França P, Moscato P (2004) A new memetic algorithm for the asymmetric traveling salesman problem. J Heur 10:483–506

    Article  MATH  Google Scholar 

  8. Chauhan P, Deep K, Pant M (2013) Novel inertia weight strategies for particle swarm optimization. Memet Comput 5:1–23

    Article  Google Scholar 

  9. Cheung G (2009) A discrete stereotyped particle swarm optimization algorithm for quadratic assignment problems. Master’s thesis, the Graduate School of Binghamton State University of New York

  10. Clerc M, Kennedy J (2002) The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Article  Google Scholar 

  11. Codenotti B, Manzini G, Margara L, Resta G (1993) Perturbation: An efficient technique for the solution of very large instances of the Euclidean TSP. INFORMS J Comput 8(2):125–133

    Article  Google Scholar 

  12. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1:3–18

    Article  Google Scholar 

  13. Dorigo M, Maniezzo V, Colorni A (1996) The ant system: Optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26(1):29–41

  14. Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, Cambridge

  15. Drezner Z (2003) A new genetic algorithm for the quadratic assignment problem. INFORMS J Comput 15(3):320–330

    Article  MATH  MathSciNet  Google Scholar 

  16. Drezner Z (2005) The extended concentric tabu for the quadratic assignment problem. Eur J Oper Res 160(2):416–422

    Article  MATH  MathSciNet  Google Scholar 

  17. Drezner Z (2008) Extensive experiments with hybrid genetic algorithms for the solution of the quadratic assignment problem. Comput Oper Res 35(3):717–736

    Article  MATH  MathSciNet  Google Scholar 

  18. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In Proceedings of the 1995 international symposium on micro machine and human science, (MHS ’95), pp 39–43

  19. Elshafei AN (1977) Hospital layout as a quadratic assignment problem. Oper Res Q (1970–1977) 28(1):167–179

    Article  MATH  Google Scholar 

  20. Engelbrecht AP (2007) Computational intelligence: An introduction. Wiley, New York

  21. França PM, Mendes A, Moscato P (2001) A memetic algorithm for the total tardiness single machine scheduling problem. Eur J Oper Res 132(1):224–242

    Article  MATH  Google Scholar 

  22. Gambardella LM, Taillard E, Dorigo M (1999) Ant colonies for the quadratic assignment problem. J Oper Res Soc 50(2):167–176

    Article  MATH  Google Scholar 

  23. Geoffrion AM, Graves GW (1976) Scheduling parallel production lines with changeover costs: Practical application of a quadratic assignment/LP approach. Oper Res 24(4):595–610

  24. Gilmore PC (1962) Optimal and suboptimal algorithms for the quadratic assignment problem. J Soc Ind Appl Math 10(2):305–313

    Article  MATH  MathSciNet  Google Scholar 

  25. Glover F (1989) Tabu search-Part I. ORSA J Comput 1(3):190–206

  26. Glover F (1990) Tabu search-Part II. ORSA J Comput 2(1):4–32

    Article  MATH  Google Scholar 

  27. Glover F, Marti R (2006) Tabu search. In: Alba E, Marti R (eds) Metaheuristic procedures for training neural networks, volume 36 of operations research/computer science interfaces series, Springer, pp 53–69

  28. Gorges-Schleuter M (1977) Asparagos96 and the traveling salesman problem. In Proceedings IEEE international conference on evolutionary computation, pp 171–174

  29. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70

    MATH  MathSciNet  Google Scholar 

  30. Hoos H, Stützle T (2004) Stochastic local search: Foundations and applications. Morgan Kaufmann, San Francisco

  31. Huntley CL, Brown DE (1991) A parallel heuristic for quadratic assignment problems. Comput Oper Res 18(3):275–289

    Article  MATH  Google Scholar 

  32. Huntley CL, Brown DE (1996) Parallel genetic algorithms with local search. Comput Oper Res 23(6):559–571

    Article  MATH  Google Scholar 

  33. James T, Rego C, Glover F (2005) Sequential and parallel path-relinking algorithms for the quadratic assignment problem. IEEE Intell Syst 20(4):58–65

    Article  Google Scholar 

  34. James T, Rego C, Glover F (2009) Multistart tabu search and diversification strategies for the quadratic assignment problem. IEEE Trans Syst Man Cybern Part A Syst Humans 39(3):579–596

    Article  Google Scholar 

  35. Janson S, Middendorf M (2003) A hierarchical particle swarm optimizer. In: Proceedings of the 2003 IEEE congress on evolutionary computation, (CEC ’03), vol 2. pp 770–776

  36. Janson S, Middendorf M (2005) A hierarchical particle swarm optimizer and its adaptive variant. IEEE Trans Syst Man Cybern Part B Cybern 35(6):1272–1282

    Article  Google Scholar 

  37. Jin N, Rahmat-Samii Y (2005) Parallel particle swarm optimization and finite-difference time-domain (PSO/FDTD) algorithm for multiband and wide-band patch antenna designs. IEEE Trans Antenna Propag 53(11):3459–3468

    Article  Google Scholar 

  38. Kennedy J, Eberhart R (1997) A discrete binary version of the particle swarm algorithm. In: Proceedings of the 1997 IEEE international conference on systems, man, and cybernetics, vol 5. pp 4104–4108

  39. Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufmann, San Francisco

    Google Scholar 

  40. Kim Y, Keely S, Ghosh J, Ling H (2007) Application of artificial neural networks to broadband antenna design based on a parametric frequency model. IEEE Trans Antennas Propag 55(3):669–674

    Article  Google Scholar 

  41. Lawler EL (1963) The quadratic assignment problem. Manag Sci 9(4):586–599

    Article  MATH  MathSciNet  Google Scholar 

  42. Liu B, Wang L, Jin Y-H (2007) An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans Sys Man Cybern Part B Cybern 37(1):18–27

    Article  Google Scholar 

  43. Liu H, Abraham A, Zhang J (2007) A particle swarm approach to quadratic assignment problems. In: Saad A, Dahal K, Sarfraz M, Roy R (eds) Soft computing inindustrial applications. Advances in soft computing, vol 39. Springer, Heidelberg, pp 213–222

  44. Loiola EM, de Abreu NMM, Boaventura-Netto PO, Hahn P, Querido T (2007) A survey for the quadratic assignment problem. Eur J Oper Res 176(2):657–690

    Article  MATH  Google Scholar 

  45. Maniezzo V, Colorni A (1999) The ant system applied to the quadratic assignment problem. IEEE Trans Knowl Data Eng 11(5):769–778

    Article  Google Scholar 

  46. Marzetta A, Brüngger A (1999) A dynamic-programming bound for the quadratic assignment problem. In: Asano T, Imai H, Lee D, Nakano S-I, Tokuyama T (eds) Computing and combinatorics. Lecture notes in computer science, vol 1627. Springer, pp 339–348

  47. Merz P, Freisleben B (1997) A genetic local search approach to the quadratic assignment problem. In: Proceedings of the 7th international conference on genetic algorithms, pp 465–472

  48. Merz P, Freisleben B (1999) A comparison of memetic algorithms, tabu search, and ant colonies for the quadratic assignment problem. In: Proceedings of the 1999 congress on evolutionary computation, vol 3. pp 2063–2070

  49. Merz P, Freisleben B (2000) Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Trans Evol Comput 4(4):337–352

    Article  Google Scholar 

  50. Misevicius A (2003) Genetic algorithm hybridized with ruin and recreate procedure: Application to the quadratic assignment problem. Knowl Based Syst 16(5–6):261–268

    Article  Google Scholar 

  51. Misevicius A (2004) An improved hybrid genetic algorithm: New results for the quadratic assignment problem. Knowl Based Syst 17(2–4):65–73

    Article  Google Scholar 

  52. Misevicius A (2005) A tabu search algorithm for the quadratic assignment problem. Comput Opt Appl 30(1):95–111

    Article  MATH  MathSciNet  Google Scholar 

  53. Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Technical report 826, Caltech concurrent computation program

  54. Moscato P (1993) An introduction to population approaches for optimization and hierarchical objective functions: A discussion on the role of tabu search. Ann Oper Res 41(1–4):85–121

  55. Moscato P (1999) Memetic algorithms: A short introduction. In: Corne D, Dorigo M, Glover F, Dasgupta D, Moscato P, Poli R, Price KV (eds) New ideas in optimization. McGraw-Hill, Maidenhead, pp 219–234

  56. Moscato P, Cotta C (2013) A gentle introduction to memetic algorithms. In: Handbook of metaheuristics. Kluwer Academic Publishers, pp 105–144

  57. Neri F, Cotta C (2012) Memetic algorithms and memetic computing optimization: A literature review. Swarm Evol Comput 2:1–14

  58. Neri F, Cotta C, Moscato P (2012) Handbook of memetic algorithms, volume 379 of studies in computational intelligence. Springer, Berlin

    Book  Google Scholar 

  59. Ni J, Li L, Qiao F, Wu Q (2013) A novel memetic algorithm and its application to data clustering. Memet Comput 5(1):65–78

    Article  Google Scholar 

  60. Nissen V (1994) Solving the quadratic assignment problem with clues from nature. IEEE Trans Neural Netw 5(1):66–72

    Article  Google Scholar 

  61. Nissen V (1997) Quadratic assignment. In: Bäck T, Fogel DB, Michalewicz Z (eds) Handbook of evolutionary computation. IOP Publishing, Bristol

    Google Scholar 

  62. Ostrowski T, Ruoppila VT (1997) Genetic annealing search for index assignment in vector quantization. Pattern Recognit Lett 18(4):311–318

  63. Pan I, Das S (2013) Design of hybrid regrouping PSO-GA based sub-optimal networked control system with random packet losses. Memet Comput 5(2):141–153

  64. Pardalos PM, Qian T, Resende MGC (1994) A greedy randomized adaptive search procedure for the quadratic assignment problem. In quadratic assignment and related problems, volume 16 of DIMACS series on discrete mathematics and theoretical computer science, pp 237–261. American Mathematical Society, 1994

  65. Burkard SKRE, Rendl F (1997) QAPLIB—a quadratic assignment problem library. http://www.seas.upenn.edu/qaplib/

  66. Rego C, James T, Glover F (2010) An ejection chain algorithm for the quadratic assignment problem. Networks 56(3):188–206

    Article  MATH  MathSciNet  Google Scholar 

  67. Sahni S, Gonzalez T (1976) P-complete approximation problems. J ACM 23(3):555–565

    Article  MATH  MathSciNet  Google Scholar 

  68. Steinberg L (1961) The backboard wiring problem: A placement algorithm. SIAM Rev 3(1):37–50

  69. Stützle T (2006) Iterated local search for the quadratic assignment problem. Eur J Oper Res 174(3):1519–1539

    Article  MATH  Google Scholar 

  70. Stützle T, Dorigo M (1999) ACO algorithms for the quadratic assignment problem. In: Corne D, Dorigo M, Glover F, Dasgupta D, Moscato P, Poli R, Price KV (eds) New ideas in optimization. McGraw-Hill, Maidenhead, pp 33–50

    Google Scholar 

  71. Stützle T, Hoos HH (2000) MAX-MIN ant system. Futur Gener Comput Syst 16(9):889–914

    Article  Google Scholar 

  72. Taillard E (1991) Robust taboo search for the quadratic assignment problem. Parallel Comput 17(4–5):443–455

    Article  MathSciNet  Google Scholar 

  73. Taillard E (2012) Homepage of Eric Taillard, 2012. http://mistic.heig-vd.ch/taillard/

  74. Tseng L-Y, Liang S-C (2006) A hybrid metaheuristic for the quadratic assignment problem. Comput Opt Appl 34(1):85–113

    Article  MATH  MathSciNet  Google Scholar 

  75. Wachowiak M, Smolikova R, Zheng Y, Zurada J, Elmaghraby A (2004) An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Trans Evol Comput 8(3):289–301

    Article  Google Scholar 

  76. Zhao M, Abraham A, Grosan C, Liu H (2008) A fuzzy particle swarm approach to multiobjective quadratic assignment problems. In: Proceedings of the second Asia international conference on modeling simulation, pp 516–521

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Acknowledgments

The partial support of a research grant from the Brandon University Research Committee is gratefully acknowledged. We would like to express our gratitude to the anonymous reviewers for their useful feedback which has improved the paper. In addition, we would like to thank Jeff Williams for useful discussions.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, American University in Cairo, Cairo, Egypt

    Ayah M. Helal

  2. Department of Mathematics and Computer Science, Brandon University, Brandon, Canada

    Ashraf M. Abdelbar

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  1. Ayah M. Helal
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Correspondence to Ashraf M. Abdelbar.

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Helal, A.M., Abdelbar, A.M. Incorporating domain-specific heuristics in a particle swarm optimization approach to the quadratic assignment problem. Memetic Comp. 6, 241–254 (2014). https://doi.org/10.1007/s12293-014-0141-y

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