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Memetic cooperative models for the tool switching problem

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Abstract

This work deals with memetic-computing agent-models based on the cooperative integration of search agents endowed with (possibly different) optimization strategies, in particular memetic algorithms. As a proof-of-concept of the model, we deploy it on the tool switching problem (ToSP), a hard combinatorial optimization problem that arises in the area of flexible manufacturing. The ToSP has been tackled by different algorithmic methods ranging from exact to heuristic methods (including local search meta-heuristics, population-based techniques and hybrids thereof, i.e., memetic algorithms). Here we consider an ample number of instances of this cooperative memetic model, whose agents are adapted to cope with this problem. A detailed experimental analysis shows that the meta-models promoting the cooperation among memetic algorithms provide the best overall results compared with their constituent parts (i.e., memetic algorithms and local search approaches). In addition, a parameter sensitivity analysis of the meta-models is developed in order to understand the interplay among the elements of the proposed topologies.

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References

  1. Hart WE, Belew RK (1991) Optimizing an arbitrary function is hard for the genetic algorithm. In: Belew RK, Booker LB (eds) 4th international conference on genetic algorithms. Morgan Kaufmann, San Mateo, CA, pp 190–195

    Google Scholar 

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

    Article  Google Scholar 

  3. Bonissone P, Subbu R, Eklund N, Kiehl T (2006) Evolutionary algorithms + domain knowledge = real-world evolutionary computation. IEEE Trans Evol Comput 10(3): 256–280

    Article  Google Scholar 

  4. Hart W, Krasnogor N, Smith J (2005) Recent advances in memetic algorithms. Studies in fuzziness and soft computing, vol 166. Springer, Berlin

    Book  Google Scholar 

  5. Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans Evol Comput 9(5): 474–488

    Article  Google Scholar 

  6. Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Technical report, Caltech concurrent computation program, report 826, California Institute of Technology, Pasadena, CA

  7. Moscato P, Cotta C (2003) A gentle introduction to memetic algorithms. In: Glover F, Kochenberger G (eds) Handbook of metaheuristics. Kluwer, Boston, MA, pp 105–144

    Google Scholar 

  8. Moscato P (1999) Memetic algorithms: a short introduction. In: Corne D, Dorigo M, Glover F (eds) New ideas in optimization. McGraw-Hill, New York, pp 219–234

    Google Scholar 

  9. Ong YS, Lim MH, Che X (2010) Memetic computation—past, present and future. IEEE Comput Intell Mag 5(2): 24–36

    Article  Google Scholar 

  10. Krasnogor N, Blackburne B, Burke E, Hirst J et al (2002) Multimeme algorithms for protein structure prediction. In: Merelo J (eds) Parallel problem solving from nature VII. Lecture notes in computer science, vol 2439. Springer, Berlin, pp 769–778

    Chapter  Google Scholar 

  11. Smith J et al (2002) Co-evolution of memetic algorithms: initial investigations. In: Merelo J (eds) Parallel problem solving from nature VII. Lecture notes in computer science, vol 2439. Springer, Berlin, pp 537–548

    Chapter  Google Scholar 

  12. Krasnogor N (2004) Self generating metaheuristics in bioinformatics: the proteins structure comparison case. Genet Program Evolv Mach 5(2): 181–201

    Article  Google Scholar 

  13. Krasnogor N, Gustafson S (2004) A study on the use of “self-generation” in memetic algorithms. Nat Comput 3(1): 53–76

    Article  MathSciNet  MATH  Google Scholar 

  14. Smith J (2007) Coevolving memetic algorithms: a review and progress report. IEEE Trans Syst Man Cybern B 37(1): 6–17

    Article  Google Scholar 

  15. Smith JE (2007) Credit assignment in adaptive memetic algorithms. In: Lipson H (ed) GECCO ’07: proceedings of the 9th annual conference on genetic and evolutionary computation conference. ACM Press, pp 1412–1419

  16. Dawkins R (1976) The selfish gene. Clarendon Press, Oxford

    Google Scholar 

  17. Radcliffe N (1994) The algebra of genetic algorithms. Ann Math Artif Intell 10: 339–384

    Article  MathSciNet  MATH  Google Scholar 

  18. Radcliffe N, Surry P (1994) Formal memetic algorithms. In: Fogarty T (eds) Evolutionary computing: AISB workshop. Lecture notes in computer science, vol 865. Springer, Berlin, pp 1–16

    Google Scholar 

  19. Toulouse M, Crainic TG, Sanso B, Thulasiraman K (1998) Self-organization in cooperative tabu search algorithms. In: Proceedings of the IEEE international conference on systems, man, and cybernetics, vol 3, pp 2379–2384

  20. Toulouse M, Thulasiraman K, Glover F (1999) Multi-level cooperative search: a new paradigm for combinatorial optimization and an application to graph partitioning. In: Amestoy P, Berger P, Daydé M, Ruiz D, Duff I, Frayssé V, Giraud L (eds) Euro-Par’99 parallel processing. Lecture notes in computer science, vol 1685. Springer, Berlin, pp 533–542

    Google Scholar 

  21. Crainic TG, Gendreau M (2002) Cooperative parallel tabu search for capacitated network design. J Heuristics 8(6): 601–627

    Article  Google Scholar 

  22. Crainic TG, Gendreau M, Hansen P, Mladenović N (2004) Cooperative parallel variable neighborhood search for the p-median. J Heuristics 10: 293–314

    Article  Google Scholar 

  23. LeBouthillier A, Crainic TG (2005) A cooperative parallel meta-heuristic for the vehicle routing problem with time windows. Comput Oper Res 32(7): 1685–1708

    Article  Google Scholar 

  24. Pelta D, Cruz C, Sancho-Royo A, Verdegay J (2006) Using memory and fuzzy rules in a co-operative multi-thread strategy for optimization. Inf Sci 176: 1849–1868

    Article  Google Scholar 

  25. Cruz C, Pelta D (2009) Soft computing and cooperative strategies for optimization. Appl Soft Comput 9(1): 30–38

    Article  Google Scholar 

  26. Lu S, Sun C (2008) Coevolutionary quantum-behaved particle swarm optimization with hybrid cooperative search. In: Computational intelligence and industrial application, PACIIA ’08, Pacific-Asia workshop, vol 1, pp 109–113

  27. Milano M, Roli A (2004) Magma: a multiagent architecture for metaheuristics. IEEE Trans Syst Man Cybern B 34(2): 925–941

    Article  Google Scholar 

  28. Malek R (2009) Collaboration of metaheuristic algorithms through a multi-agent system. In: Mark V, Strasser T, Zoitl A (eds) Holonic and multi-agent systems for manufacturing. Lecture notes in computer science, vol 5696. Springer, Berlin, pp 72–81

    Chapter  Google Scholar 

  29. Sbihi A (2010) A cooperative local search-based algorithm for the multiple-scenario max- min knapsack problem. Eur J Oper Res 202(2): 339–346

    Article  MathSciNet  MATH  Google Scholar 

  30. Cowling P, Kendall G, Soubeiga E (2008) A hyperheuristic approach to schedule a sales submit. In: Burke E, Erben W (eds) PATAT 2000. Lecture notes in computer science, vol 2079. Springer, Berlin, pp 176–190

    Google Scholar 

  31. Chakhlevitch K, Cowling P (2008) Hyperheuristics: recent developments. In: Cotta C, Sevaux M, Sörensen K (eds) Adaptive and multilevel metaheuristics. Studies in computational intelligence, vol 136. Springer, Berlin, pp 3–29

    Chapter  Google Scholar 

  32. Bradwell R, Brown KN (1999) Parallel asynchronous memetic algorithms. In: Evolutionary computation and parallel processing workshop—GECCO 1999, Orlando, FL

  33. Talukdar SN (1999) Collaboration rules for autonomous software agents. Decis Support Syst 24(3–4): 269–278

    Article  Google Scholar 

  34. Mühlenbein H (1991) Evolution in time and space—the parallel genetic algorithm. In: Rawlins GJ (eds) Foundations of genetic algorithms. Morgan Kaufmann Publishers, San Fransisco, pp 316–337

    Google Scholar 

  35. Cotta C, Mendes A, Garcia V, França P, Moscato P et al (2003) Applying memetic algorithms to the analysis of microarray data. In: Raidl G (eds) Applications of evolutionary computing. Lecture notes in computer science, vol 2611. Springer, Berlin, pp 22–32

    Google Scholar 

  36. Tang J, Lim M, Ong Y, Er M (2004) Study of migration topology in island model parallel hybrid-ga for large scale quadratic assignment problems. In: Control, automation, robotics and vision conference, vol 3, pp 2286–2291

  37. Belady L (1966) A study of replacement algorithms for virtual storage computers. IBM Syst J 5: 78–101

    Article  Google Scholar 

  38. Bard JF (1988) A heuristic for minimizing the number of tool switches on a flexible machine. IIE Trans 20(4): 382–391

    Article  Google Scholar 

  39. Tang C, Denardo E (1988) Models arising from a flexible manufacturing machine, part I: minimization of the number of tool switches. Oper Res 36(5): 767–777

    Article  MATH  Google Scholar 

  40. Shirazi R, Frizelle G (2001) Minimizing the number of tool switches on a flexible machine: an empirical study. Int J Prod Res 39(15): 3547–3560

    Article  Google Scholar 

  41. Laporte G, Salazar-González J, Semet F (2004) Exact algorithms for the job sequencing and tool switching problem. IIE Trans 36(1): 37–45

    Article  Google Scholar 

  42. Oerlemans A (1992) Production planning for flexible manufacturing systems. PhD dissertation, University of Limburg, Maastricht

  43. Crama Y, Kolen A, Oerlemans A, Spieksma F (1994) Minimizing the number of tool switches on a flexible machine. Int J Flex Manuf Syst 6: 33–54

    Article  Google Scholar 

  44. Hertz A, Laporte G, Mittaz M, Stecke K (1998) Heuristics for minimizing tool switches when scheduling part types on a flexible machine. IIE Trans 30: 689–694

    Google Scholar 

  45. Djellab H, Djellab K, Gourgand M (2000) A new heuristic based on a hypergraph representation for the tool switching problem. Int J Prod Econ 64(1–3): 165–176

    Article  Google Scholar 

  46. Hertz A, Widmer M (1993) An improved tabu search approach for solving the job shop scheduling problem with tooling constraints. Discrete Appl Math 65: 319–345

    Article  MathSciNet  Google Scholar 

  47. Al-Fawzan M, Al-Sultan K (2003) A tabu search based algorithm for minimizing the number of tool switches on a flexible machine. Comput Ind Eng 44(1): 35–47

    Article  Google Scholar 

  48. Zhou BH, Xi LF, Cao YS (2005) A beam-search-based algorithm for the tool switching problem on a flexible machine. Int J Adv Manuf Technol 25(9–10): 876–882

    Article  Google Scholar 

  49. Amaya JE, Cotta C, Fernández AJ (2008) A memetic algorithm for the tool switching problem. In: Blesa M, Blum C, Cotta C, Fernández A, Gallardo J, Roli A, Sampels M (eds) Hybrid metaheuristics 2008. Lecture notes in computer science, vol 5296. Springer, Berlin, pp 190–202

    Google Scholar 

  50. Amaya JE, Cotta C, Fernández AJ (2010) Hybrid cooperation models for the tool switching problem. In: González J, Pelta D, Cruz C, Terrazas G, Krasnogor N (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in computational intelligence, vol 284. Springer, Berlin, pp 39–52

    Chapter  Google Scholar 

  51. Tzur M, Altman A (2004) Minimization of tool switches for a flexible manufacturing machine with slot assignment of different tool sizes. IIE Trans 36(2): 95–110

    Article  Google Scholar 

  52. Mladenovic N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24(11): 1097–1100

    Article  MathSciNet  MATH  Google Scholar 

  53. Larrañaga P, Kuijpers CMH, Murga RH, Inza I, Dizdarevic S (1999) Genetic algorithms for the travelling salesman problem: A review of representations and operators. Artif Intell Rev 13: 129–170

    Article  Google Scholar 

  54. Cotta C, Troya J (1998) Genetic forma recombination in permutation flowshop problems. Evol Comput 6(1): 25–44

    Article  Google Scholar 

  55. Ong YS, Keane A (2004) Meta-lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8(2): 99–110

    Article  Google Scholar 

  56. Ong YS, Lim MH, Zhu N, Wong K (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern B 36(1): 141–152

    Article  Google Scholar 

  57. Tang J, Lim M, Ong Y (2007) Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Comput 11: 873–888

    Article  Google Scholar 

  58. Neri F, Toivanen J, Mäkinen RAE (2007) An adaptive evolutionary algorithm with intelligent mutation local searchers for designing multidrug therapies for hiv. Appl Intell 27(3): 219–235

    Article  Google Scholar 

  59. Caponio A, Cascella GL, Neri F, Salvatore N, Sumner M (2007) A fast adaptive memetic algorithm for online and offline control design of pmsm drives. IEEE Trans Syst Man Cybern B 37(1): 28–41

    Article  Google Scholar 

  60. Tirronen V, Neri F, Kärkkäinen T, Majava K, Rossi T (2008) An enhanced memetic differential evolution in filter design for defect detection in paper production. Evol Comput 16(4): 529–555

    Article  Google Scholar 

  61. Caponio A, Neri F, Tirronen V (2009) Super-fit control adaptation in memetic differential evolution frameworks. Soft Comput 13(8–9): 811–831

    Article  Google Scholar 

  62. Talbi EG, Bachelet V (2006) Cosearch: A parallel cooperative metaheuristic. J Math Model Algorithms 5(1): 5–22

    Article  MathSciNet  MATH  Google Scholar 

  63. Lehmann E, D’Abrera H (1998) Nonparametrics: statistical methods based on ranks. Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  64. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200): 675–701

    Article  Google Scholar 

  65. Iman R, Davenport J (1980) Approximations of the critical region of the Friedman statistic. Commun Stat 9: 571–595

    Article  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  67. Alba E, Troya JM (2000) Influence of the migration policy in parallel distributed gas with structured and panmictic populations. Appl Intell 12: 163–181

    Article  Google Scholar 

  68. Krause W, Sollacher R, Greiner M et al (2005) Self-* topology control in wireless multihop ad hoc communciations networks. In: Babaoglu O (eds) Self-star properties in complex information systems. Lecture notes in computer science, vol 3460. Springer, Berlin, pp 49–62

    Chapter  Google Scholar 

  69. Smith JE (2008) Self-adaptation in evolutionary algorithms for combinatorial optimisation. In: Cotta C, Sevaux M, Sörensen K (eds) Adaptive and multilevel metaheuristics. Studies in computational intelligence, vol 136. Springer, Berlin, pp 31–57

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Laboratorio de Computación de Alto Rendimiento, Universidad Nacional Experimental del Táchira, Av. Universidad, Paramillo, San Cristóbal, Venezuela

    Jhon Edgar Amaya

  2. Dept. Lenguajes y Ciencias de la Computación, ETSI Informática, University of Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Carlos Cotta & Antonio J. Fernández-Leiva

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  1. Jhon Edgar Amaya
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  2. Carlos Cotta
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  3. Antonio J. Fernández-Leiva
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Correspondence to Carlos Cotta.

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Amaya, J.E., Cotta, C. & Fernández-Leiva, A.J. Memetic cooperative models for the tool switching problem. Memetic Comp. 3, 199–216 (2011). https://doi.org/10.1007/s12293-011-0059-6

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