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A black-box scatter search for optimization problems with integer variables

Author

Listed:
  • Manuel Laguna
  • Francisco Gortázar
  • Micael Gallego
  • Abraham Duarte
  • Rafael Martí

Abstract

The goal of this work is the development of a black-box solver based on the scatter search methodology. In particular, we seek a solver capable of obtaining high quality outcomes to optimization problems for which solutions are represented as a vector of integer values. We refer to these problems as integer optimization problems. We assume that the decision variables are bounded and that there may be constraints that require that the black-box evaluator is called in order to know whether they are satisfied. Problems of this type are common in operational research areas of applications such as telecommunications, project management, engineering design and the like.Our experimental testing includes 171 instances within four classes of problems taken from the literature. The experiments compare the performance of the proposed method with both the best context-specific procedures designed for each class of problem as well as context-independent commercial software. The experiments show that the proposed solution method competes well against commercial software and that can be competitive with specialized procedures in some problem classes. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Manuel Laguna & Francisco Gortázar & Micael Gallego & Abraham Duarte & Rafael Martí, 2014. "A black-box scatter search for optimization problems with integer variables," Journal of Global Optimization, Springer, vol. 58(3), pages 497-516, March.
    • Handle: RePEc:spr:jglopt:v:58:y:2014:i:3:p:497-516
    DOI: 10.1007/s10898-013-0061-2
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    References listed on IDEAS

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    1. Taillard, Eric D. & Gambardella, Luca M. & Gendreau, Michel & Potvin, Jean-Yves, 2001. "Adaptive memory programming: A unified view of metaheuristics," European Journal of Operational Research, Elsevier, vol. 135(1), pages 1-16, November.
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    3. M Laguna & J Molina & F Pérez & R Caballero & A G Hernández-Díaz, 2010. "The challenge of optimizing expensive black boxes: a scatter search/rough set theory approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 53-67, January.
    4. James C. Bean, 1994. "Genetic Algorithms and Random Keys for Sequencing and Optimization," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 154-160, May.
    5. Vicente Campos & Manuel Laguna & Rafael Martí, 2005. "Context-Independent Scatter and Tabu Search for Permutation Problems," INFORMS Journal on Computing, INFORMS, vol. 17(1), pages 111-122, February.
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    Cited by:

    1. Sancho Salcedo-Sanz & Leo Carro-Calvo & Mercè Claramunt & Ana Castañer & Maite Mármol, 2014. "Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms," Risks, MDPI, vol. 2(2), pages 1-14, April.
    2. Nikolaos Ploskas & Nikolaos V. Sahinidis, 2022. "Review and comparison of algorithms and software for mixed-integer derivative-free optimization," Journal of Global Optimization, Springer, vol. 82(3), pages 433-462, March.
    3. Hu, Lu & Liu, Yang, 2016. "Joint design of parking capacities and fleet size for one-way station-based carsharing systems with road congestion constraints," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 268-299.
    4. Boukouvala, Fani & Misener, Ruth & Floudas, Christodoulos A., 2016. "Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO," European Journal of Operational Research, Elsevier, vol. 252(3), pages 701-727.
    5. Miguel A. González & Juan José Palacios & Camino R. Vela & Alejandro Hernández-Arauzo, 2017. "Scatter search for minimizing weighted tardiness in a single machine scheduling with setups," Journal of Heuristics, Springer, vol. 23(2), pages 81-110, June.

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