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

TL;DR

This paper introduces a reinforcement learning framework for dynamically managing search operators in meta-heuristics, specifically applied to permutation flowshop scheduling, enhancing adaptability and performance without expert input.

Contribution

It presents a novel RL-based framework that adaptively manages search operators in meta-heuristics, eliminating the need for expert knowledge and improving scheduling performance.

Findings

Outperforms state-of-the-art algorithms in optimality gap

Achieves faster convergence in scheduling problems

Effectively adapts operator portfolio during search

Abstract

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.