Performance Analysis of Path Relinking on Many-objective NK-Landscapes

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. Its unique characteristic of performing the search in the objective and decision spaces makes it interesting to study its behavior in many objective optimization. In this paper, we focus on the behavior of pure path relinking, propose several variants of the path relinking that vary on their strategies of selecting solutions, and analyze its performance using several many-objective NK-landscapes as instances. In general, results of the study show that the path relinking becomes more effective in improving the convergence of the algorithm as we increase the number of objectives. Also, it is shown that the selection strategy associated to path relinking plays an important role to emphasize either convergence or spread of the algorithm. This study provides useful insights for practitioners on how to exploit path relinking to enhance multi-objective evolutionary algorithms for complex combinatorial optimization problems.

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