Performance Dynamics of Bio-Inspired Metaheuristics: A Comparative Study

Abstract

Bio-inspired optimisation leverages the principles of biological systems to address complex computational challenges. This paper provides an analytical study of the field, categorising major techniques based on their evolution, underlying mechanisms, and performance. By comparing these modern approaches with traditional legacy methods, the study highlights the unique strengths and limitations of nature-inspired algorithms. This review serves as a foundational resource for researchers seeking to identify current trends and future directions in optimisation research. Furthermore, the paper synthesises recent empirical data to evaluate the scalability and convergence speed of these algorithms across diverse datasets. Ultimately, this survey serves as a strategic roadmap, identifying current research lacunae and proposing future directions for the development of hybrid and hyper-heuristic bio-inspired frameworks.