Performance of an Intensification Strategy Based on Learning in a Metaheuristic: Meta-RaPS with Path Relinking

Arif Arin () and Ghaith Rabadi ()
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Arif Arin: Middle East Technical University, Universiteler Mahallesi
Ghaith Rabadi: Old Dominion University

Chapter Chapter 6 in Heuristics, Metaheuristics and Approximate Methods in Planning and Scheduling, 2016, pp 109-126 from Springer

Abstract: 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.

Keywords: Intensification; Path relinking; Meta-RaPS; Metaheuristics; Knapsack problem (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-319-26024-2_6

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DOI: 10.1007/978-3-319-26024-2_6

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