mlsanityData to 14 Oct 2026

Dynamic operator management in meta-heuristics using reinforcement learning: an application to permutation flowshop scheduling problems

arXiv 2408.14864 ↗First posted 27 Aug 2024cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

This study develops a framework based on reinforcement learning to dynamically manage a large portfolio of search operators within meta-heuristics. Using the idea of tabu search, the framework allows for continuous adaptation by temporarily excluding less efficient operators and updating the portfolio composition during the search. A Q-learning-based adaptive operator selection mechanism is used to select the most suitable operator from the dynamically updated portfolio at each stage. Unlike traditional approaches, the proposed framework requires no input from the experts regarding the search operators, allowing domain-specific non-experts to effectively use the framework. The performance of the proposed framework is analyzed through an application to the permutation flowshop scheduling problem. The results demonstrate the superior performance of the proposed framework against state-of-the-art algorithms in terms of optimality gap and convergence speed.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 49 references that are in the map. First posted on arXiv 27 Aug 2024.

Builds on

2 of 49 references.

References · 2

  1. 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 623
  2. 2014An Experimental Study of Adaptive Control for Evolutionary Algorithmscited by 2

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