Adaptive operator selection utilising generalised experience

TL;DR

This paper introduces a reinforcement learning-based adaptive operator selection framework to improve the balance of exploration and exploitation in combinatorial optimisation problems, demonstrating promising experimental results.

Contribution

It presents a novel, scalable RL-based approach for adaptive operator selection that generalises experience utilisation in optimisation algorithms.

Findings

Experimental results show improved optimisation performance.

The approach effectively balances exploration and exploitation.

The framework demonstrates scalability and adaptability.

Abstract

Optimisation problems, particularly combinatorial optimisation problems, are difficult to solve due to their complexity and hardness. Such problems have been successfully solved by evolutionary and swarm intelligence algorithms, especially in binary format. However, the approximation may suffer due to the the issues in balance between exploration and exploitation activities (EvE), which remain as the major challenge in this context. Although the complementary usage of multiple operators is becoming more popular for managing EvE with adaptive operator selection schemes, a bespoke adaptive selection system is still an important topic in research. Reinforcement Learning (RL) has recently been proposed as a way to customise and shape up a highly effective adaptive selection system. However, it is still challenging to handle the problem in terms of scalability. This paper proposes and assesses a RL-based novel approach to help develop a generalised framework for gaining, processing, and utilising the experiences for both the immediate and future use. The experimental results support the proposed approach with a certain level of success.