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Parameter Tuning Problem in Metaheuristics: A Self-Adaptive Local Search Algorithm for Combinatorial Problems

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Women in Industrial and Systems Engineering

Part of the book series: Women in Engineering and Science ((WES))

Abstract

Combinatorial optimization is an important mathematical topic that consists of finding an optimal solution from a finite set of search space. Many problems encountered in real life are defined as combinatorial optimization. There is an increasing interest among researchers to develop heuristic algorithms for combinatorial optimization problems, because enumeration based search is not feasible for them. So, obtaining global optimum solutions for these problems, within a reasonable time, is extremely difficult by exact algorithms. Particularly in recent years, high-level metaheuristics have been developed for combinatorial optimization problems. On the other hand, it is known that metaheuristic algorithms are controlled by a set of parameters. The best parameter set reveals better performance such as solution quality and computer times. The process to find the best parameter set is called parameter optimization or parameter tuning that requires a deep learning of the problem structure or a roughly trial-and-error process. An alternative way for tuning is to control parameters through the running of the algorithms. Those algorithms utilize some feedback from the search and change the parameter values adaptively depending on the knowledge. There is a great deal to develop adaptive algorithms for combinatorial optimization problems to overcome the difficulties of parameter tuning. While a survey is carried out about parameter tuning approaches for metaheuristics, the performance of a new self-adaptive local search (SALS) algorithm is introduced in this chapter, and investigated for the vehicle routing problem considering both single and multi-objectives on a large scale suit of test problems.

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References

  • Adenso-Diaz B, Laguna M (2006) Fine-tuning of algorithms using fractional experimental design and local search. Oper Res 54(1):99–114

    Article  Google Scholar 

  • Alabas C (2004) Self-controlled local search heuristic for combinatorial optimization problems. PhD theses. Gazi University, Ankara

    Google Scholar 

  • Alabas-Uslu C (2008) A self-tuning heuristic for a multi-objective vehicle routing problem. J Oper Res Soc 59(7):988–996

    Article  Google Scholar 

  • Alabas-Uslu C, Dengiz B (2014) A self-adaptive heuristic algorithm for combinatorial optimization problems. Int J Comput Int Sys 7(5):827–852

    Article  Google Scholar 

  • Arin A, Rabadi G, Unal R (2011) Comparative studies on design of experiments for tuning parameters in a genetic algorithm for a scheduling problem. Int J Exp Design Process Optim 2(2):103–124

    Google Scholar 

  • Balaprakash P, Birattari M, Stutzle T (2007) Improvement strategies for the F-race algorithm: sampling design and iterative refinement. In: BartzBeielstein T, Blesa M, Blum C, Naujoks B, Roli A, Rudolph G, Sampels M (eds) 4th International Workshop on Hybrid Metaheuristics, Proceedings, HM 2007. Lecture Notes in Computer Science, vol 4771. Springer, Berlin, pp 108–122

    Google Scholar 

  • Barbosa EBM, Senne ELF, Silva MB (2015) Improving the performance of metaheuristics: an approach combining response surface methodology and racing algorithms. Int J Eng Math 2015:9. https://doi.org/10.1155/2015/167031

    Article  MATH  Google Scholar 

  • Bartz-Beielstein T (2006) Experimental research in evolutionary computation: the new experimentalism, Natural Computing Series. Springer Verlag, Berlin

    MATH  Google Scholar 

  • Battiti R, Brunato M (2010) Reactive search optimization: learning while optimizing. Chap. In: Gendreau M, Potvin JY (eds) Handbook of metaheuristics, 2nd edn. Springer, Berlin

    Google Scholar 

  • Battiti R, Tecchiolli G (1994) The reactive tabu search. INFORMS J Comput 6(2):126–140

    Article  Google Scholar 

  • Battiti R, Brunato M, Mascia F (2008) Reactive search and intelligent optimization, Operations research/Computer Science Interfaces, vol 45. Springer, Berlin

    MATH  Google Scholar 

  • Birattari M, Stutzle T, Paquete L and Varrentrapp K (2002). A racing algorithm for configuring metaheuristics. Proceedings of the Genetic and Evolutionary Computation Conference, 11–18, GECCO’02

    Google Scholar 

  • Christofides N, Elion S (1969) An algorithm for the vehicle dispatching problem. Oper Res Quart 20:309–318

    Article  Google Scholar 

  • Corberán A, Fernández E, Laguna M, Martí R (2002) Heuristic solutions to the problem of routing school buses with multiple objectives. J Oper Res Soc 53(4):427–435

    Article  Google Scholar 

  • Coy SP, Golden BL, Runger GC, Wasil EA (2000) Using experimental design to find effective parameter settings for heuristics. J Heuristics 7:77–97

    Article  Google Scholar 

  • De Jong K (2007) Parameter settings in EAs: a 30 year perspective. In: Lobo FG, Lima CF, Michalewicz Z (eds) Parameter setting in evolutionary algorithms, Studies in computational intelligence. Springer, Berlin/Heidelberg, pp 1–18

    Google Scholar 

  • Dengiz B, Alabas-Uslu C (2015) A self-tuning heuristic for design of communication networks. J Oper Res Soc 66(7):1101–1114

    Article  Google Scholar 

  • Dengiz B, Alabas-Uslu C, Sabuncuoğlu I (2009) A local search heuristic with self-tuning parameter for permutation flow-shop scheduling problem. In: IEEE Symposium on Computational Intelligence in Scheduling. CI-Sched’09, April 2–March 30, Nashville, TN, pp 62–67

    Google Scholar 

  • Dobslaw F (2010) A parameter tuning framework for metaheuristics based on design of experiments and artificial neural networks. In: Proceedings of the International Conference on Computer Mathematics and Natural Computing, pp 1–4

    Google Scholar 

  • Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp 39–43

    Google Scholar 

  • Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evol Comput 3(2):124–141

    Article  Google Scholar 

  • Eiben AE, Michalewicz Z, Schoenauer M, Smith JE (2007) Parameter control in evolutionary algorithms. In: Lobo FG, Lima CF, Michalewicz Z (eds) Parameter setting in evolutionary algorithms. Studies in computational intelligence. Springer, Berlin/Heidelberg, pp 19–46

    Google Scholar 

  • Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13:533–549

    Article  MathSciNet  Google Scholar 

  • Golden BL, Wasil EA, Kelly JP, Chao I-M (1998) The impact of metaheuristics on solving the vehicle routing problem: algorithms, problem sets, and computational results. In: Crainic TG, Laporte G (eds) Fleet management and logistics. Kluwer, Boston

    Google Scholar 

  • Groër C, Golden B, Wasil E (2011) A parallel algorithm for the vehicle routing problems. INFORMS J Comput 23:315–330

    Article  MathSciNet  Google Scholar 

  • Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evol Comput 9(2):159–195

    Article  Google Scholar 

  • Harik GR, Lobo FG (1999) A parameter-less genetic algorithm. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) GECCO-99: proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, SanFrancisco, pp 258–267

    Google Scholar 

  • Hutter F, Hoos H, Stutzle T (2007) Automatic algorithm configuration based on local search. In: Proceedings of the twenty-second conference on Artificial intelligence (AAAI’07), pp 1152–1157

    Google Scholar 

  • Hutter F, Hoos HH, Leyton-Brown K, Stützle T (2009) ParamILS: an automatic algorithm configuration framework. J Artif Intell Res 36:267–306

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Li F, Golden B, Wasil E (2005) Vey large-scale vehicle routing: new test problems, algorithms, and results. Comput Oper Res 32:1165–1179

    Article  Google Scholar 

  • Lima CF, Lobo FG (2004) Parameter-less optimization with the extended compact genetic algorithm and iterated local search. In: Proceedings of the genetic and evolutionary computation conference GECCO-2004, part I, LNCS 3102, Springer, pp 1328–1339

    Google Scholar 

  • Lobo FG, Goldberg DE (2004) Parameter-less genetic algorithm in practice. Inf Sci 167(217):232

    MATH  Google Scholar 

  • López-Ibáñez M, Dubois-Lacoste J, Cáceres LP, Birattari M, Stützle T (2016) The irace package: iterated racing for automatic algorithm configuration. Oper Res Perspect 3:43–58

    Article  MathSciNet  Google Scholar 

  • Meissner M, Schmuker M, Schneider G (2006) Optimized particle swarm optimization (OPSO) and its application to artificial neural network training. BMC Bioinform 7:125. https://doi.org/10.1186/1471-2105-7-125

    Article  Google Scholar 

  • Mester D, Braysy O (2007) Active-guided evolution strategies for large-scale capacitated vehicle routing problems. Comput Oper Res 34:2964–2975

    Article  Google Scholar 

  • Nadi F, Khader AT (2011) A parameter-less genetic algorithm with customized crossover and mutation operators. In: Proceedings of the 13th annual conference on genetic and evolutionary computation, pp 901–908

    Google Scholar 

  • Nannen V, Eiben AE (2006) A method for parameter calibration and relevance estimation in evolutionary algorithms. In: Proceedings of genetic and evolutionary computation conference, GECCO 2006, ACM, pp 183–190

    Google Scholar 

  • Neumüller C, Wagner S, Kronberger G, Affenzeller M (2011) Parameter meta-optimization of metaheuristic optimization algorithms. In: Proceedings of the 13th international conference on Computer Aided Systems Theory, EUROCAST'11, Part I, Las Palmas de Gran Canaria, Spain, pp 367–374

    Chapter  Google Scholar 

  • Pacheco J, Marti R (2006) Tabu search for a multi-objective routing problem. J Oper Res Soc 57:29–37

    Article  Google Scholar 

  • Prins C (2004) A simple and effective evolutionary algorithm for the vehicle routing problem. Comput Oper Res 31:1985–2002

    Article  MathSciNet  Google Scholar 

  • Reimann M, Doerner K, Hartl RF (2004) D-ants: saving based ants divide and conquer the vehicle routing problem. Comput Oper Res 31(4):563–591

    Article  Google Scholar 

  • Ries J, Beullens P, Salt D (2012) Instance-specific multi-objective parameter tuning based on fuzzy logic. Eur J Oper Res 218:305–315

    Article  Google Scholar 

  • Robert H, Zbigniew M, Thomas CP (1996) Self-adaptive genetic algorithm for numeric functions. In: Proceedings of the 4th international conference on parallel problem solving from nature, Springer-Verlag

    Google Scholar 

  • Sait SM, Youssef H (1999) Iterative computer algorithms with applications in engineering. In: IEEE computer society, Los–Alamitos

    Google Scholar 

  • Silberholz J, Golden B (2010) Comparison of metaheuristics. In: Gendreau M, Potvin JY (eds) Handbook of metaheuristics, 2nd edn. Springer, Berlin

    MATH  Google Scholar 

  • Smith JE (2003) Co-evolving memetic algorithms: a learning approach to robust scalable optimisation. In: Proceedings of the 2003 congress on evolutionary computation, pp 498–505

    Google Scholar 

  • Smith SK, Eiben AE (2009) Comparing parameter tuning methods for evo-lutionary algorithms. In: IEEE Congress on Evolutionary Computation, pp 399–406

    Google Scholar 

  • Sörensen K, Sevausx M, Glover F (2017) A history of metaheuristics. arXiv preprint arXiv:1704.00853

    Google Scholar 

  • Tarantilis CD (2005) Solving the vehicle routing problem with adaptive memory programming methodology. Comput Oper Res 32(9):2309–2327

    Article  MathSciNet  Google Scholar 

  • Tarantilis CD, Kiranoudis CT (2002) BoneRoute: an adaptive memory-based method for effective fleet management. Ann Oper Res 115(1):227–241

    Article  MathSciNet  Google Scholar 

  • Tarantilis CD, Kiranoudis CT, Vassiliadis VS (2002a) A backtracking adaptive threshold accepting metaheuristic method for the vehicle routing problem. Syst Anal Model Simul 42(5):631–644

    Article  MathSciNet  Google Scholar 

  • Tarantilis CD, Kiranoudis CT, Vassiliadis VS (2002b) A list based threshold accepting algorithm for the capacitated vehicle routing problem. J Comput Math 79(5):537–553

    MATH  Google Scholar 

  • Toth P, Vigo D (2003) The granular tabu search (and its application to the vehicle routing problem). INFORMS J Comput 15(4):333–348

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  • Xu J, Kelly JP (1996) A network flow-based tabu search heuristic for the vehicle routing problem. Transp Sci 30(4):379–393

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Marmara University, Istanbul, Turkey

    Cigdem Alabas-Uslu

  2. Department of Industrial Engineering, Başkent University, Ankara, Turkey

    Berna Dengiz

Authors
  1. Cigdem Alabas-Uslu
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  2. Berna Dengiz
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Correspondence to Berna Dengiz .

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Editors and Affiliations

  1. Department of Industrial and Systems Engineering, Auburn University, Auburn, AL, USA

    Alice E. Smith

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Alabas-Uslu, C., Dengiz, B. (2020). Parameter Tuning Problem in Metaheuristics: A Self-Adaptive Local Search Algorithm for Combinatorial Problems. In: Smith, A. (eds) Women in Industrial and Systems Engineering. Women in Engineering and Science. Springer, Cham. https://doi.org/10.1007/978-3-030-11866-2_5

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  • DOI: https://doi.org/10.1007/978-3-030-11866-2_5

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