Soft Computing

, Volume 21, Issue 18, pp 5193–5206 | Cite as

The Problem Aware Local Search algorithm: an efficient technique for permutation-based problems

  • Gabriela F. Minetti
  • Gabriel Luque
  • Enrique Alba
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Abstract

In this article, we will examine whether the Problem Aware Local Search, an efficient method initially proposed for the DNA Fragment Assembly Problem, can also be used in other application domains and with other optimization problems. The main idea is to maintain the key features of PALS and apply it to different permutation-based combinatorial problems. In order to carry out a comprehensive analysis, we use a wide benchmark of well-known problems with different kinds of variation operators and fitness functions, such as the Quadratic Assignment Problem, the Flow Shop Scheduling Problem, and the Multiple Knapsack Problem. We also discuss the main design alternatives for building an efficient and accurate version of PALS for these problems in a competitive manner. In general, the results show that PALS can achieve high-quality solutions for these problems and do it efficiently.

Keywords

Metaheuristic Problem Aware Local Search Optimization problems Quadratic Assignment Problem Flow Shop Scheduling Problem Multiple Knapsack Problem 

Notes

Acknowledgements

The first author is supported by the Universidad Nacional de La Pampa, and the ANPCYT under contract PICTO 2011-0278 and the Incentive Program. The second and third authors have been partially funded by Project Number 8.06/5.47.4142 in collaboration with the VSB-Technical University of Ostrava and by the Spanish MINECO Project TIN2014-57341-R (http://moveon.lcc.uma.es).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Abdelkafi O, Idoumghar L, Lepagnot J (2015) Distributed multistart hybrid iterative tabu search. In: 2015 IEEE international conference on systems, man, and cybernetics, pp 1962–1967. doi:10.1109/SMC.2015.342
  2. Alba E, Dorronsoro B (2008) Cellular genetic algorithms. Operations research/computer science interfaces. Springer, HeidelbergMATHGoogle Scholar
  3. Alba E, Luque G (2007) A new local search algorithm for the DNA fragment assembly problem. In: Evolutionary computation in combinatorial optimization, EvoCOP’07, Lecture notes in computer science, vol 4446. Springer, Valencia, pp 1–12Google Scholar
  4. Beasley JE (1990) OR-library: distributing test problems by electronic mail. J Oper Res Soc 41(11):1069–1072CrossRefGoogle Scholar
  5. Benlic U, Hao JK (2015) Memetic search for the quadratic assignment problem. Expert Syst Appl 42(1):584–595. doi:10.1016/j.eswa.2014.08.011 CrossRefGoogle Scholar
  6. Bhaba RS, Wilbert EW, Gary LH (1998) Locating sets of identical machines in a linear layout. Ann Oper Res 77:183–207CrossRefMATHGoogle Scholar
  7. Boyer V, Elkihel M, Baz DE (2009) Heuristics for the 0–1 multidimensional knapsack problem. Eur J Oper Res 199(3):658–664MathSciNetCrossRefMATHGoogle Scholar
  8. Burkard RE, Karisch SE, Rendl F (1997) Qaplib—a quadratic assignment problem library. J Glob Optim 10(4):391–403MathSciNetCrossRefMATHGoogle Scholar
  9. Chicano F, Luque G, Alba E (2012) Autocorrelation measures for the quadratic assignment problem. Appl Math Lett 25(4):698–705MathSciNetCrossRefMATHGoogle Scholar
  10. Colombo G, Mumford C (2005) Comparing algorithms, representations and operators for the multi-objective knapsack problem. In: 2005. The 2005 IEEE congress on evolutionary computation, vol 2, pp 1268–1275. doi:10.1109/CEC.2005.1554836
  11. Coy SP, Golden BL, Runger GC, Wasil EA (1998) See the forest before the trees: fine-tuned learning and its application to the traveling salesman problem. IEEE Trans Syst Man Cybern Part A 28:454–464CrossRefGoogle Scholar
  12. Dorronsoro B, Alba E, Luque G, Bouvry P (2008) A self-adaptive cellular memetic algorithm for the DNA fragment assembly problem. IEEE Congr Evol Comput 2008:2651–2658Google Scholar
  13. Fogel DB (1988) An evolutionary approach to the traveling salesman problem. Biol Cybern 60:139–144MathSciNetCrossRefGoogle Scholar
  14. Fogel DB, Atmar J (1990) Comparing genetic operators with Gaussian mutations in simulated evolutionary processes using linear systems. Biol Cybern 63:111–114CrossRefGoogle Scholar
  15. Fukunaga A, Tazoe S (2009) Combining multiple representations in a genetic algorithm for the multiple knapsack problem. In: 2009. CEC ’09. IEEE congress on evolutionary computation, pp 2423–2430Google Scholar
  16. Iturriaga S, Nesmachnow S, Luna F, Alba E (2015) A parallel local search in CPU/GPU for scheduling independent tasks on large heterogeneous computing systems. J Supercomput 71(2):648–672. doi:10.1007/s11227-014-1315-6 CrossRefGoogle Scholar
  17. James T, Rego C, Glover F (2009) Multistart tabu search and diversification strategies for the quadratic assignment problem. Trans Syst Man Cybern Part A 39(3):579–596. doi:10.1109/TSMCA.2009.2014556 CrossRefGoogle Scholar
  18. Koopmans T, Beckmann M (1957) Assignment problems and the location of economic activities. Econometrica 25(1):53–76MathSciNetCrossRefMATHGoogle Scholar
  19. Martello S, Toth P (1981) Heuristic algorithms for the multiple knapsack problem. Computing 27(2):93–112MathSciNetCrossRefMATHGoogle Scholar
  20. Mason A, Rönnqvist M (1998) Solution methods for the balancing of jet turbines. Comput Oper Res 24:153–167CrossRefMATHGoogle Scholar
  21. Minetti G, Alba E (2010) Metaheuristic assemblers of DNA strands: noiseless and noisy cases. In: IEEE congress on evolutionary computation. IEEE, pp 1–8Google Scholar
  22. Minetti G, Leguizamón G, Alba E (2012) SAX: a new and efficient assembler for solving DNA fragment assembly problem. In: JAIIO (ed) 13th Argentine symposium on artificial intelligence, ASAI 2012. SADIO, pp 177–188Google Scholar
  23. Minetti G, Leguizamón G, Alba E (2014) An improved trajectory-based hybrid metaheuristic applied to the noisy DNA fragment assembly problem. Inf Sci 277:273–283MathSciNetCrossRefGoogle Scholar
  24. Mumford C (2003) Comparing representations and recombination operators for the multi-objective 0/1 knapsack problem. In: 2003. CEC ’03. The 2003 congress on evolutionary computation, vol 2, pp 854–861. doi:10.1109/CEC.2003.1299756
  25. Norris M, Lecavalier L (2010) Evaluating the use of exploratory factor analysis in developmental disability psychological research. J Autism Dev Disord 40(1):8–20. doi:10.1007/s10803-009-0816-2 CrossRefGoogle Scholar
  26. Polak GG (2005) On a special case of the quadratic assignment problem with an application to storage-and-retrieval devices. Ann Oper Res 138:223–233MathSciNetCrossRefMATHGoogle Scholar
  27. Pop M, Salzberg S, Shumway M (2002) Genome sequence assembly: algorithms and issues. Computer 35(7):47–54CrossRefGoogle Scholar
  28. Rego C (1998) Relaxed tours and path ejections for the traveling salesman problem. Eur J Oper Res 106(2):522–538CrossRefMATHGoogle Scholar
  29. Zhang Q, Sun J, Tsang E (2005) An evolutionary algorithm with guided mutation for the maximum clique problem. IEEE Trans Evol Comput 9(2):192–200CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Gabriela F. Minetti
    • 1
  • Gabriel Luque
    • 2
  • Enrique Alba
    • 2
  1. 1.Faculty of EngineeringNational University of La PampaGeneral PicoArgentina
  2. 2.Universidad de MálagaMálagaEspaña

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