Recent Advancements in Metaheuristic-based Approaches for Solving Multi-Objective Optimization Problems

The present manuscript investigates the key notions of metaheuristics for multiobjective optimisation problems. It comprehensively analyses the foundations of multi-objective optimisation, the need to optimise multiple conflicting objectives, the constraints of traditional methods, and the role of metaheuristics in this context. In addition to these, the paper also provides an exhaustive literature review encompassing major facets of multi-objective optimisation, relevant interludes and challenges, and the rationale for the existing review. The paper aims to cover classical multi-objective optimisation techniques and their role in tackling complex real-world problems. Techniques such as PSO, NSGA-II, SPEA2, MOEA/D and many others are discussed in the paper. These approaches mentioned in the literature survey are scrutinised intensely. Recent advancements, such as hybridisation strategies, such as Hybrid EAs, adaptive and auto-tuning algorithms and parallel and distributed metaheuristics, successfully form a part of the paper. The paper also includes an important case study on an improved NSGA-II algorithm based on reinforcement learning for aircraft assembly line balancing and scheduling. This paper explores the diverse applications in engineering design, environmental management and energy optimisation, supply chain and logistics, and healthcare and medical treatment planning. Overall, the paper aims to revisit the pre-existing data, study all techniques and present future scopes.