Deep memetic models for combinatorial optimization problems: application to the tool switching problem

  • Jhon Edgar Amaya
  • Carlos Cotta
  • Antonio J. Fernández-Leiva
  • Pablo García-SánchezEmail author
Regular Research Paper


Memetic algorithms are techniques that orchestrate the interplay between population-based and trajectory-based algorithmic components. In particular, some memetic models can be regarded under this broad interpretation as a group of autonomous basic optimization algorithms that interact among them in a cooperative way in order to deal with a specific optimization problem, aiming to obtain better results than the algorithms that constitute it separately. Going one step beyond this traditional view of cooperative optimization algorithms, this work tackles deep meta-cooperation, namely the use of cooperative optimization algorithms in which some components can in turn be cooperative methods themselves, thus exhibiting a deep algorithmic architecture. The objective of this paper is to demonstrate that such models can be considered as an efficient alternative to other traditional forms of cooperative algorithms. To validate this claim, different structural parameters, such as the communication topology between the agents, or the parameter that influences the depth of the cooperative effort (the depth of meta-cooperation), have been analyzed. To do this, a comparison with the state-of-the-art cooperative methods to solve a specific combinatorial problem, the Tool Switching Problem, has been performed. Results show that deep models are effective to solve this problem, outperforming metaheuristics proposed in the literature.


Deep architecture Hybrid algorithms Memetic algorihms Tool switching problem (ToSP) 



The authors wish to thank the anonymous reviewers for their helpful comments. The first author thanks to the Decanato de Investigación of UNET the partial support of the present research. Second and third author were partially supported by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech, and also by research projects Ephemech ( (TIN2014-56494-C4-1-P), and DeepBio ( (TIN2017-85727-C4-01-P), funded by Ministerio Español de Economía y Competitividad. Fourth author was also partially supported by “Ayuda del Programa de Fomento e Impulso de la Actividad Investigadora de la Universidad de Cádiz”.


  1. 1.
    Al-Fawzan MA, Al-Sultan KS (2003) A tabu search based algorithm for minimizing the number of tool switches on a flexible machine. Comput Ind Eng 44(1):35–47Google Scholar
  2. 2.
    Aldous D, Vazirani UV (1994) “Go with the winners” algorithms. In: 35th annual symposium on foundations of computer science, Santa Fe, New Mexico, USA, IEEE Computer Society, pp 492–501, , 20–22 Nov 1994Google Scholar
  3. 3.
    Amaya JE, Cotta C, Fernández AJ (2008) A memetic algorithm for the tool switching problem. In: Blesa MJ, Blum C, Cotta C, Fernández AJ, Gallardo JE, Roli A, Sampels M (eds) Hybrid metaheuristics, 5th international workshop, HM 2008, Málaga, Proceedings, Lecture Notes in Computer Science, vol 5296, pp 190–202, Springer, Spain, 8–9 Oct 2008Google Scholar
  4. 4.
    Amaya JE, Cotta C, Fernández Leiva AJ (2010) Hybrid cooperation models for the tool switching problem. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Nature inspired cooperative strategies for optimization, NICSO 2010, Studies in Computational Intelligence, vol 284, pp 39–52, , Granada, Spain, Springer, 12–14 May 2010Google Scholar
  5. 5.
    Amaya JE, Cotta C, Fernández Leiva AJ (2011) Memetic cooperative models for the tool switching problem. Memet Comput 3(3):199–216Google Scholar
  6. 6.
    Amaya JE, Cotta C, Fernández Leiva AJ (2012) Solving the tool switching problem with memetic algorithms. AI EDAM 26(2):221–235Google Scholar
  7. 7.
    Amaya JE, Cotta C, Fernández Leiva AJ (2013) Cross entropy-based memetic algorithms: an application study over the tool switching problem. Int J Comput Intell Syst 6(3):559–584Google Scholar
  8. 8.
    Anandalingam G, Friesz TL (1992) Hierarchical optimization: an introduction. Ann OR 34:1–11MathSciNetzbMATHGoogle Scholar
  9. 9.
    Babaoglu O, Jelasity M, Montresor A, Fetzer C, Leonardi S, van Moorsel A, van Steen M (eds) (2005) Self-star properties in complex information systems, lecture notes in computer science, vol 3460. Springer, BerlinGoogle Scholar
  10. 10.
    Bard JF (1988) A heuristic for minimizing the number of tool switches on a flexible machine. IIE Trans 20(4):382–391Google Scholar
  11. 11.
    Berns A, Ghosh S (2009) Dissecting self-\(\star \) properties. In: 3rd IEEE international conference on self-adaptive and self-organizing systems—SASO 2009. IEEE Press, San Francisco, CA, pp 10–19Google Scholar
  12. 12.
    Byrski A, Schaefer R, Smolka M, Cotta C (2013) Asymptotic guarantee of success for multi-agent memetic systems. Bull Pol Acad Sci Tech Sci 61(1):257–278Google Scholar
  13. 13.
    Camacho D, Lara-Cabrera R, Merelo Guervós JJ, Castillo PA, Cotta C, Fernández Leiva AJ, Fernández de Vega F, Chávez de la OF (2018) From ephemeral computing to deep bioinspired algorithms: new trends and applications. Future Gener Comput Syst 88:735–746Google Scholar
  14. 14.
    Corona CC, Pelta DA (2009) Soft computing and cooperative strategies for optimization. Appl Soft Comput 9(1):30–38Google Scholar
  15. 15.
    Crainic TG, Toulouse M (2007) Explicit and emergent cooperation schemes for search algorithms. In: Maniezzo V, Battiti R, Watson J (eds) Learning and Intelligent Optimization 2007, Lecture Notes in Computer Science, Springer, vol 5313, pp 95–109Google Scholar
  16. 16.
    Crainic TG, Gendreau M, Hansen P, Mladenovic N (2004) Cooperative parallel variable neighborhood search for the p-median. J Heuristics 10(3):293–314Google Scholar
  17. 17.
    Cui Z, Xue F, Cai X, Cao Y, Wang G, Chen J (2018) Detection of malicious code variants based on deep learning. IEEE Trans Ind Inform 14(7):3187–3196Google Scholar
  18. 18.
    Cui Z, Du L, Wang P, Cai X, Zhang W (2019) Malicious code detection based on cnns and multi-objective algorithm. J Parallel Distrib Comput 129:50–58Google Scholar
  19. 19.
    El-Abd M, Kamel M (2005) A taxonomy of cooperative search algorithms. In: Blesa MJ, Blum C, Roli A, Sampels M (eds) Hybrid metaheuristics, 2nd international workshop, HM 2005, proceedings, lecture notes in computer science, vol 3636, pp 32–41, Barcelona, Spain, Springer, 29–30 Aug 2005Google Scholar
  20. 20.
    Fernández-Leiva AJ, Gutiérrez-Fuentes Á (2019) On distributed user-centric memetic algorithms. Soft Comput 23(12):4019–4039Google Scholar
  21. 21.
    Gallardo JE, Cotta C, Fernández AJ (2007) On the hybridization of memetic algorithms with branch-and-bound techniques. IEEE Trans Syst Man Cybern Part B 37(1):77–83Google Scholar
  22. 22.
    García del Amo IJ, Pelta DA, Masegosa AD, Verdegay JL (2010) A software modeling approach for the design and analysis of cooperative optimization systems. Softw Pract Exp 40(9):811–823Google Scholar
  23. 23.
    Hertz A, Laporte G, Mittaz M, Stecke K (1998) Heuristics for minimizing tool switches when scheduling part types on a flexible machine. IIE Trans 30:689–694Google Scholar
  24. 24.
    Jourdan L, Basseur M, Talbi E (2009) Hybridizing exact methods and metaheuristics: a taxonomy. Eur J Oper Res 199(3):620–629MathSciNetzbMATHGoogle Scholar
  25. 25.
    Krasnogor N, Smith J (2001) Emergence of profitable search strategies based on a simple inheritance mechanism. In: Spector L et al (eds) Genetic and evolutionary computation conference 2001. Morgan Kaufmann, San Francisco CA, pp 432–439Google Scholar
  26. 26.
    Laporte G, Salazar-González JJ, Semet F (2004) Exact algorithms for the job sequencing and tool switching problem. IIE Trans 36(1):37–45Google Scholar
  27. 27.
    LeCun Y, Bengio Y, Hinton GE (2015) Deep learning. Nature 521(7553):436–444Google Scholar
  28. 28.
    Lim TY (2014) Structured population genetic algorithms: a literature survey. Artif Intell Rev 41(3):385–399Google Scholar
  29. 29.
    Malek R (2009) Collaboration of metaheuristic algorithms through a multi-agent system. In: Marík V, Strasser TI, Zoitl A (eds) Holonic and multi-agent systems for manufacturing, Proceedings of 4th international conference on industrial applications of holonic and multi-agent systems, HoloMAS 2009, Lecture Notes in Computer Science, Linz, Austria, vol 5696, pp 72–81, Springer, August 31–September 2 2009Google Scholar
  30. 30.
    Neri F, Cotta C (2012) Memetic algorithms and memetic computing optimization: a literature review. Swarm Evol Comput 2:1–14Google Scholar
  31. 31.
    Nogueras R, Cotta C (2014) An analysis of migration strategies in island-based multimemetic algorithms. In: Bartz-Beielstein T et al (eds) Parallel Problem Solving from Nature—PPSN XIII, lecture notes in computer science, vol 8672. Springer, Berlin, pp 731–740Google Scholar
  32. 32.
    Schaefer R, Kołodziej J (2002) Genetic search reinforced by the population hierarchy. In: Poli R, Rowe JE, Jong KAD (eds) Foundations of genetic algorithms VII. Morgan Kaufmann, Burlington, pp 383–400Google Scholar
  33. 33.
    Schaefer R, Byrski A, Kolodziej J, Smolka M (2012) An agent-based model of hierarchic genetic search. Comput Math Appl 64(12):3763–3776MathSciNetzbMATHGoogle Scholar
  34. 34.
    Talbi E, Bachelet V (2006) COSEARCH: a parallel cooperative metaheuristic. J Math Model Algorithms 5(1):5–22MathSciNetzbMATHGoogle Scholar
  35. 35.
    Tang CS, Denardo EV (1988) Models arising from a flexible manufacturing machine, part I: minimization of the number of tool switches. Oper Res 36(5):767–777zbMATHGoogle Scholar
  36. 36.
    Vasile M, Ricciardi LA (2017) Multi agent collaborative search. In: Schütze O, Trujillo L, Legrand P, Maldonado Y (eds) NEO 2015—results of the numerical and evolutionary optimization workshop NEO 2015 held at 23-25 Sept 2015 in Tijuana, Mexico, Springer, studies in computational intelligence, vol 663, pp 223–252Google Scholar
  37. 37.
    Wang G, Cai X, Cui Z, Min G, Chen J (2019) High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm. IEEE Trans Emerg Top Comput.
  38. 38.
    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):876–882Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Universidad Nacional Experimental del Táchira (UNET), Laboratorio de Computación de Alto Rendimiento (LCAR)San CristóbalVenezuela
  2. 2.Dept. Lenguajes y Ciencias de la Computación, ETSI InformáticaUniversity of MalagaMalagaSpain
  3. 3.Dept. de Ingeniería Informática, ESIUniversity of CádizCádizSpain

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