Path relinking on many-objective NK-landscapes

Abstract

Path relinking is a population-based heuristic that explores the trajectories in decision space between two elite solutions. It has been successfully used as a key component of several multi-objective optimizers, especially for solving bi-objective problems. In this paper, we focus on the behavior of pure path relinking, propose several variants of the path relinking that vary on their selection strategies, and analyze its performance using several many-objective NK-landscapes as instances. The study shows that the path relinking becomes more effective in improving the convergence of the algorithm as the number of objectives increases. It also shows that the selection strategy associated to path relinking plays an important role to emphasize either convergence or spread of the algorithm. © 2010 Springer-Verlag.