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Speeding up continuous GRASP

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  • Hirsch, M.J.
  • Pardalos, P.M.
  • Resende, M.G.C.

Abstract

Continuous GRASP (C-GRASP) is a stochastic local search metaheuristic for finding cost-efficient solutions to continuous global optimization problems subject to box constraints (Hirsch et al., 2007). Like a greedy randomized adaptive search procedure (GRASP), a C-GRASP is a multi-start procedure where a starting solution for local improvement is constructed in a greedy randomized fashion. In this paper, we describe several improvements that speed up the original C-GRASP and make it more robust. We compare the new C-GRASP with the original version as well as with other algorithms from the recent literature on a set of benchmark multimodal test functions whose global minima are known. Hart's sequential stopping rule (1998) is implemented and C-GRASP is shown to converge on all test problems.

Suggested Citation

  • Hirsch, M.J. & Pardalos, P.M. & Resende, M.G.C., 2010. "Speeding up continuous GRASP," European Journal of Operational Research, Elsevier, vol. 205(3), pages 507-521, September.
    • Handle: RePEc:eee:ejores:v:205:y:2010:i:3:p:507-521
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    References listed on IDEAS

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    1. Hedar, Abdel-Rahman & Fukushima, Masao, 2006. "Tabu Search directed by direct search methods for nonlinear global optimization," European Journal of Operational Research, Elsevier, vol. 170(2), pages 329-349, April.
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    Cited by:

    1. C. J. Price & M. Reale & B. L. Robertson, 2021. "Oscars-ii: an algorithm for bound constrained global optimization," Journal of Global Optimization, Springer, vol. 79(1), pages 39-57, January.
    2. Kammerdiner, A.R. & Pasiliao, E.L., 2014. "In and out forests on combinatorial landscapes," European Journal of Operational Research, Elsevier, vol. 236(1), pages 78-84.
    3. Tiago Maritan Ugulino Araújo & Lisieux Marie M. S. Andrade & Carlos Magno & Lucídio Anjos Formiga Cabral & Roberto Quirino Nascimento & Cláudio N. Meneses, 2016. "DC-GRASP: directing the search on continuous-GRASP," Journal of Heuristics, Springer, vol. 22(4), pages 365-382, August.
    4. Ivorra, Benjamin & Mohammadi, Bijan & Manuel Ramos, Angel, 2015. "A multi-layer line search method to improve the initialization of optimization algorithms," European Journal of Operational Research, Elsevier, vol. 247(3), pages 711-720.
    5. László Pál, 2017. "Empirical study of the improved UNIRANDI local search method," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(4), pages 929-952, December.
    6. Emile Glorieux & Bo Svensson & Fredrik Danielsson & Bengt Lennartson, 2017. "Constructive cooperative coevolution for large-scale global optimisation," Journal of Heuristics, Springer, vol. 23(6), pages 449-469, December.
    7. Morales-Enciso, Sergio & Branke, Juergen, 2015. "Tracking global optima in dynamic environments with efficient global optimization," European Journal of Operational Research, Elsevier, vol. 242(3), pages 744-755.
    8. Ricardo Silva & Mauricio Resende & Panos Pardalos, 2014. "Finding multiple roots of a box-constrained system of nonlinear equations with a biased random-key genetic algorithm," Journal of Global Optimization, Springer, vol. 60(2), pages 289-306, October.
    9. R. M. A. Silva & M. G. C. Resende & P. M. Pardalos, 2015. "A Python/C++ library for bound-constrained global optimization using a biased random-key genetic algorithm," Journal of Combinatorial Optimization, Springer, vol. 30(3), pages 710-728, October.
    10. Michael J. Hirsch & Daniel E. Schroeder & Alvaro Maggiar & Irina S. Dolinskaya, 2014. "Multi-depot vessel routing problem in a direction dependent wavefield," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 38-57, July.

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