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Comparison of Metaheuristics

In: Handbook of Metaheuristics

Author

Listed:
  • John Silberholz

    (Center for Scientific Computing and Mathematical Modeling, University of Maryland)

  • Bruce Golden

    (R.H. Smith School of Business, University of Maryland)

Abstract

Metaheuristics are truly diverse in nature—under the overarching theme of performing operations to escape local optima, algorithms as different as ant colony optimization, tabu search, harmony search, and genetic algorithms have emerged. Due to the unique functionality of each type of metaheuristic, comparison of metaheuristics is in many ways more difficult than other algorithmic comparisons. In this chapter, we discuss techniques for meaningful comparison of metaheuristics. We discuss how to create and classify instances in a new testbed and how to make sure other researchers have access to the problems for future metaheuristic comparisons. Further, we discuss the disadvantages of large parameter sets and how to measure complicating parameter interactions in a metaheuristic’s parameter space. Last, we discuss how to compare metaheuristics in terms of both solution quality and runtime.

Suggested Citation

  • John Silberholz & Bruce Golden, 2010. "Comparison of Metaheuristics," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 625-640, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-1665-5_21
    DOI: 10.1007/978-1-4419-1665-5_21
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    Citations

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    Cited by:

    1. Luis Henrique Pauleti Mendes & Fábio Luiz Usberti & Celso Cavellucci, 2022. "The Capacitated and Economic Districting Problem," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2003-2016, July.
    2. Michael Schneider & Michael Drexl, 2017. "A survey of the standard location-routing problem," Annals of Operations Research, Springer, vol. 259(1), pages 389-414, December.
    3. Gregorio Tirado & Lars Magnus Hvattum, 2017. "Determining departure times in dynamic and stochastic maritime routing and scheduling problems," Flexible Services and Manufacturing Journal, Springer, vol. 29(3), pages 553-571, December.
    4. Drexl, Michael & Schneider, Michael, 2015. "A survey of variants and extensions of the location-routing problem," European Journal of Operational Research, Elsevier, vol. 241(2), pages 283-308.
    5. Emmanuel Okewu & Sanjay Misra & Rytis Maskeliūnas & Robertas Damaševičius & Luis Fernandez-Sanz, 2017. "Optimizing Green Computing Awareness for Environmental Sustainability and Economic Security as a Stochastic Optimization Problem," Sustainability, MDPI, vol. 9(10), pages 1-17, October.
    6. A. S. Santos & A. M. Madureira & M. L. R. Varela, 2018. "The Influence of Problem Specific Neighborhood Structures in Metaheuristics Performance," Journal of Mathematics, Hindawi, vol. 2018, pages 1-14, July.
    7. Iain Dunning & Swati Gupta & John Silberholz, 2018. "What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 608-624, August.
    8. Seyedehzahra NEMATOLLAHI & Giancarlo MANZI, 2018. "Portfolio Management Using Prospect Theory: Comparing Genetic Algorithms and Particle Swarm Optimization," Departmental Working Papers 2018-03, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    9. Tu, Wei & Fang, Zhixiang & Li, Qingquan & Shaw, Shih-Lung & Chen, BiYu, 2014. "A bi-level Voronoi diagram-based metaheuristic for a large-scale multi-depot vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 84-97.
    10. Elena Niculina Dragoi & Vlad Dafinescu, 2021. "Review of Metaheuristics Inspired from the Animal Kingdom," Mathematics, MDPI, vol. 9(18), pages 1-52, September.

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