Highly Configurable Evolutionary Multi‐Objective Optimisation and Application in UAV Path Planning

Abstract

ABSTRACT Multi‐objective optimisation is crucial in engineering and real‐world decision‐making. Although traditional decomposition‐based approaches such as MOEA/D yield reasonable results, they depend heavily on expert knowledge. Existing MetaBBO approaches partially alleviate this reliance, however, they offer limited configurability and lack real‐world validation. To address these limitations, we propose EMO‐CRL, a highly configurable Evolutionary Multi‐objective Optimisation framework through Collaborative Reinforcement Learning. We first construct a comprehensive configuration space encompassing multiple advanced MOEA/D mechanisms, effectively creating a unified structure for exploring diverse algorithmic variants. Recognising the inherent complexity of the high‐dimensional configuration space, EMO‐CRL then innovatively decomposes it into six sub‐spaces to enable efficient exploration. This decomposition is coupled with the formulation of dynamic algorithm configuration as a multi‐agent Markov Decision Process, featuring comprehensive state representation and finer‐grained action space. EMO‐CRL employs Value‐Decomposition Networks to collaboratively adapt agents across diverse scenarios, allowing effective knowledge sharing and improved decision‐making. Extensive evaluations on WFG and DTLZ benchmarks show that EMO‐CRL outperforms several state‐of‐the‐art MOEA/D variants. Furthermore, its application to UAV path planning highlights its practical effectiveness.