Path relinking for large-scale global optimization

  • Authors:

  • Abraham Duarte;Rafael Martí;Francisco Gortazar

  • Affiliations:

  • Universidad Rey Juan Carlos, Departamento de Ciencias de la Computación, Madrid, Spain;Universidad de Valencia, Departamento de Estadística e Investigación Operativa, Valencia, Spain;Universidad Rey Juan Carlos, Departamento de Ciencias de la Computación, Madrid, Spain

  • Venue:
  • Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
  • Year:
  • 2011

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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.