Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue European Journal of Operational Research Année : 2022

Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem

Résumé

This paper aims at integrating machine learning techniques into meta-heuristics for solving combinatorial optimization problems. Specifically, our study develops a novel efficient iterated greedy algorithm based on reinforcement learning. The main novelty of the proposed algorithm is its new perturbation mechanism, which incorporates Q-learning to select appropriate perturbation operators during the search process. Through an application to the permutation flowshop scheduling problem, comprehensive computational experiments are conducted on a wide range of benchmark instances to evaluate the performance of the proposed algorithm. This evaluation is done against non-learning versions of the iterated greedy algorithm and seven state-of-the-art algorithms from the literature. The experimental results and statistical analyses show the better performance of the proposed algorithm in terms of optimality gaps, convergence rate, and computational overhead.

Dates et versions

hal-03723715 , version 1 (15-07-2022)

Identifiants

Citer

Maryam Karimi-Mamaghan, Mehrdad Mohammadi, Bastien Pasdeloup, Patrick Meyer. Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 2022, ⟨10.1016/j.ejor.2022.03.054⟩. ⟨hal-03723715⟩
63 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More