The Artificial Bee Colony Algorithm: A Comprehensive Survey of Variants, Modifications, Applications, Developments, and Opportunities

Abstract

Meta-heuristic algorithms aim to achieve near-optimal solutions to complex optimization problems by taking inspiration from nature. The last three decades have seen an increased focus on the use of meta-heuristics in optimization, with the direct result that a great number of new meta-heuristics have been created to tackle challenging real-world situations in various sectors. Swarm intelligence is one of the most important families of bio-inspired algorithms and the artificial bee colony (ABC) algorithm is a prominent member. This paper presents a comprehensive survey of the ABC algorithm and describes its variants, modifications, applications, and developments. The primary purpose of this survey is to provide a complete analysis of the current developments in the ABC algorithm which will include improvements, variations, hybridizations, multi-objectives, and its applications in a variety of domains. This research presents the results of several studies that have been carried out to improve the ABC algorithm’s performance in various fields using different methodologies. Finally, we discuss the future opportunities and challenges for ABC algorithm research, including potential areas for further development and the need for rigorous testing and benchmarking. We conclude that the ABC algorithm is a promising and versatile optimization algorithm that has the potential to be applied to a wide range of real-world problems.