Skip to main content
SpringerLink
Log in

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

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • 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

  • Alba E, Dorronsoro B (2008) Cellular genetic algorithms. Operations research/computer science interfaces. Springer, Heidelberg

    MATH  Google Scholar 

  • 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 1–12

  • Beasley JE (1990) OR-library: distributing test problems by electronic mail. J Oper Res Soc 41(11):1069–1072

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Bhaba RS, Wilbert EW, Gary LH (1998) Locating sets of identical machines in a linear layout. Ann Oper Res 77:183–207

    Article  MATH  Google Scholar 

  • Boyer V, Elkihel M, Baz DE (2009) Heuristics for the 0–1 multidimensional knapsack problem. Eur J Oper Res 199(3):658–664

    Article  MathSciNet  MATH  Google Scholar 

  • Burkard RE, Karisch SE, Rendl F (1997) Qaplib—a quadratic assignment problem library. J Glob Optim 10(4):391–403

    Article  MathSciNet  MATH  Google Scholar 

  • Chicano F, Luque G, Alba E (2012) Autocorrelation measures for the quadratic assignment problem. Appl Math Lett 25(4):698–705

    Article  MathSciNet  MATH  Google Scholar 

  • 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

  • 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 28:454–464

    Article  Google Scholar 

  • 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 2008:2651–2658

    Google Scholar 

  • Fogel DB (1988) An evolutionary approach to the traveling salesman problem. Biol Cybern 60:139–144

    Article  MathSciNet  Google Scholar 

  • Fogel DB, Atmar J (1990) Comparing genetic operators with Gaussian mutations in simulated evolutionary processes using linear systems. Biol Cybern 63:111–114

    Article  Google Scholar 

  • 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 2423–2430

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Koopmans T, Beckmann M (1957) Assignment problems and the location of economic activities. Econometrica 25(1):53–76

    Article  MathSciNet  MATH  Google Scholar 

  • Martello S, Toth P (1981) Heuristic algorithms for the multiple knapsack problem. Computing 27(2):93–112

    Article  MathSciNet  MATH  Google Scholar 

  • Mason A, Rönnqvist M (1998) Solution methods for the balancing of jet turbines. Comput Oper Res 24:153–167

    Article  MATH  Google Scholar 

  • Minetti G, Alba E (2010) Metaheuristic assemblers of DNA strands: noiseless and noisy cases. In: IEEE congress on evolutionary computation. IEEE, pp 1–8

  • 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 177–188

  • Minetti G, Leguizamón G, Alba E (2014) An improved trajectory-based hybrid metaheuristic applied to the noisy DNA fragment assembly problem. Inf Sci 277:273–283

    Article  MathSciNet  Google Scholar 

  • 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

  • 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

    Article  Google Scholar 

  • Polak GG (2005) On a special case of the quadratic assignment problem with an application to storage-and-retrieval devices. Ann Oper Res 138:223–233

    Article  MathSciNet  MATH  Google Scholar 

  • Pop M, Salzberg S, Shumway M (2002) Genome sequence assembly: algorithms and issues. Computer 35(7):47–54

    Article  Google Scholar 

  • Rego C (1998) Relaxed tours and path ejections for the traveling salesman problem. Eur J Oper Res 106(2):522–538

    Article  MATH  Google Scholar 

  • Zhang Q, Sun J, Tsang E (2005) An evolutionary algorithm with guided mutation for the maximum clique problem. IEEE Trans Evol Comput 9(2):192–200

    Article  Google Scholar 

Download references

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).

Author information

Authors and Affiliations

  1. Faculty of Engineering, National University of La Pampa, General Pico, Argentina

    Gabriela F. Minetti

  2. Universidad de Málaga, Málaga, España

    Gabriel Luque & Enrique Alba

Authors
  1. Gabriela F. Minetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gabriel Luque
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Enrique Alba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gabriela F. Minetti.

Ethics declarations

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.

Additional information

Communicated by F. Xhafa.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Minetti, G.F., Luque, G. & Alba, E. The Problem Aware Local Search algorithm: an efficient technique for permutation-based problems. Soft Comput 21, 5193–5206 (2017). https://doi.org/10.1007/s00500-017-2515-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-017-2515-9

Keywords

Search

Navigation