A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Performance scaling of multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Local search guided by path relinking and heuristic bounds
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
International Journal of Hybrid Intelligent Systems
Hi-index | 0.00 |
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.