Monarch butterfly optimization: A comprehensive review. (15th April 2021)

• Record Type: Journal Article
• Authors: Feng, Yanhong; Deb, Suash; Wang, Gai-Ge; Alavi, Amir H.
• Is Part Of: Expert systems with applications. Volume 168(2021)
• Publication Date: 2021-04-15
• Subjects: Evolutionary computations -- Monarch butterfly optimization -- Swarm intelligence -- Metaheuristic -- Optimization; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.114418
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A comprehensive review of the monarch butterfly algorithm is proposed. The different variants based on monarch butterfly algorithm are analyzed. The hybridizations of monarch butterfly algorithm are reviewed. The applications of monarch butterfly algorithm are described. Abstract: Swarm intelligence (SI) is the collective behavior of decentralized, self-organized natural or artificial systems. Monarch butterfly optimization (MBO) algorithm is a class of swarm intelligence metaheuristic algorithm inspired by the migration behavior of monarch butterflies. Through the migration operation and butterfly adjusting operation, individuals in MBO are updated. MBO can outperform many state-of-the-art optimization techniques when solving global numerical optimization and engineering problems. This paper presents a comprehensive review of the MBO algorithm including its modifications, hybridizations, variants, and applications. Additionally, further research directions for MBO are discussed. This review study serves as a solid reference for future studies in the arena of SI and in particular the MBO algorithm.