A Study on the Working of Swarm-Based Metaheuristic Algorithms Using Theoretical Framework

Swarm intelligence, one of the subfields of metaheuristics, has emerged as one of the active research fields in the scientific community. This popularity is due to their ability of solving multimodal, nonlinear, and non-convex optimization problems with greater efficiency. Numerous metaheuristic algorithms based on swarm intelligence have been proposed till now, and it is predicted that the count will further be increased in the advancing years. This leads to the necessity of continuous analysis and study of recently proposed algorithms. In this paper, a framework has been proposed for the theoretical understanding of the working of swarm-based metaheuristic algorithms. The 11 recently proposed swarm-based metaheuristic algorithms (from the year 2017–2022) are reviewed according to that framework. Additionally, this paper also gives an insight into the various challenges associated with swarm-based algorithms along with the future directions.