Recent Advances and Applications of the Multi-verse Optimiser Algorithm: A Survey from 2020 to 2024

Abstract

The multi-verse optimiser (MVO) algorithm, inspired by the metaphor of multiple universes and their interactions, has emerged as a promising metaheuristic optimisation technique. This review paper provides an in-depth analysis of the MVO algorithm and its progression throughout the years, with a particular focus on developments from 2020 to 2024. We begin by elucidating the fundamental principles and components of MVO, highlighting its unique characteristics and historical context. Subsequently, we delve into recent advancements, modifications, and hybridisation of MVO with other optimisation methods, illustrating how these innovations have enhanced its performance and applicability. Our survey encompasses a broad range of publications that have employed MVO and its variants, examining its efficacy across diverse problem domains. We discuss empirical studies that benchmark MVO against other optimisation algorithms, providing insights into its strengths and limitations. Furthermore, we address prevalent criticisms and challenges faced by MVO, along with potential avenues for improvement and resolution. Real-world applications of MVO across various fields are showcased, emphasising its impact and utility in solving complex optimisation problems. We analyse how MVO has been adapted to tackle specific challenges in engineering, finance, logistics, and beyond. Finally, we outline prospective research directions aimed at refining the efficiency and effectiveness of the MVO algorithm, including avenues for exploring novel hybridisation and theoretical enhancements. This review is a significant resource for scholars and practitioners aiming to comprehend the latest developments, applications, and prospects of the MVO algorithm.