An exploration for higher efficiency in multi objective optimisation with reinforcement learning
This addresses efficiency issues in optimization algorithms for researchers and practitioners, though it appears incremental as it extends single-objective methods to multi-objective cases.
The paper tackles the challenge of improving efficiency in multi-objective optimization by proposing a multi-objective reinforcement learning approach to optimize sequences of operators, aiming to enhance performance in search processes.
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.