An exploration for higher efficiency in multi objective optimisation with reinforcement learning

TL;DR

This paper investigates using multi-objective reinforcement learning to improve efficiency in multi-objective optimization by optimizing operator sequences, addressing a gap in existing research.

Contribution

It proposes a generalized multi-objective reinforcement learning approach to enhance optimization efficiency, extending prior single-objective methods to multi-objective scenarios.

Findings

Preliminary stages completed demonstrating potential benefits.

Framework shows promise for improved operator sequencing.

Further phases are needed for comprehensive validation.

Abstract

Efficiency in optimisation and search processes persists to be one of the challenges, which affects the performance and use of optimisation algorithms. Utilising a pool of operators instead of a single operator to handle move operations within a neighbourhood remains promising, but an optimum or near optimum sequence of operators necessitates further investigation. One of the promising ideas is to generalise experiences and seek how to utilise it. Although numerous works are done around this issue for single objective optimisation, multi-objective cases have not much been touched in this regard. A generalised approach based on multi-objective reinforcement learning approach seems to create remedy for this issue and offer good solutions. This paper overviews a generalisation approach proposed with certain stages completed and phases outstanding that is aimed to help demonstrate the efficiency of using multi-objective reinforcement learning.