Path relinking for large-scale global optimization

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. 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 1000, totalizing 95 instances. Our results show that the evolutionary path relinking 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.