Abstract
Intensification and diversification in metaheuristics are two main strategies to enhance the search process and solution quality. In Meta-RaPS (Metaheuristic for Randomized Priority Search), a recent memoryless metaheuristic, intensification and diversification strategies are controlled only by the level of randomness specified by its parameters. We introduce in this paper a Path Relinking (PR) learning algorithm and integrate it into Meta-RaPS to intelligently enhance its intensification capability by learning “good” attributes of the best solutions. To evaluate its performance, the proposed Meta-RaPS PR is tested on the 0-1 Multidimensional Knapsack Problem (MKP). The results show that applying PR as an intensification strategy in Meta-RaPS is very effective as it outperformed other approaches used in the literature with this problem. The PR approach also transformed the memoryless nature of Meta-RaPS into an “intelligent” algorithm.
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