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

• URL: http://arxiv.org/abs/2512.10208v1
• Date: Thu, 11 Dec 2025 01:58:04 GMT
• Title: An exploration for higher efficiency in multi objective optimisation with reinforcement learning
• Authors: Mehmet Emin Aydin,
• Abstract summary: Efficiency in optimisation and search processes persists to be one of the challenges.<n>Utilising a pool of operators instead of a single operator to handle move operations within a neighbourhood remains promising.<n>One of the promising ideas is to generalise experiences and seek how to utilise it.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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