Abstract
In this paper we consider the problem of finding a global optimum of a multimodal function applying path relinking. In particular, we target unconstrained large-scale problems and compare two variants of this methodology: the static and the evolutionary path relinking (EvoPR). Both are based on the strategy of creating trajectories of moves passing through high-quality solutions in order to incorporate their attributes to the explored solutions. Computational comparisons are performed on a test-bed of 19 global optimization functions previously reported with dimensions ranging from 50 to 1,000, totalizing 95 instances. Our results show that the EvoPR procedure is competitive with the state-of-the-art methods in terms of the average optimality gap achieved. Statistical analysis is applied to draw significant conclusions.
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Acknowledgments
This research has been partially supported by the Ministerio de Educación y Ciencia of Spain (TIN2009-07516). We would like to thank Profs. Glover and Resende for their descriptions and suggestions on the Path Relinking and Evolutionary Path Relinking methodologies, respectively.
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Duarte, A., Martí, R. & Gortazar, F. Path relinking for large-scale global optimization. Soft Comput 15, 2257–2273 (2011). https://doi.org/10.1007/s00500-010-0650-7
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DOI: https://doi.org/10.1007/s00500-010-0650-7