Comparative Study of Different Metaheuristics on CEC 2020 Benchmarks

Metaheuristic algorithms have increased in usage in all the scientific fields during the last decades. Since no optimisation algorithm is valid for all optimisation problems, many metaheuristics have been developed for various applications. Accordingly, this paper presents a comparative study on CEC 2020 optimisation problems among different algorithms. The goal is to give an overall sight of selecting a specific metaheuristic algorithm for a particular application. The algorithms in this study are; dynamic differential annealed optimisation, particle swarm optimisation, fertilisation optimisation algorithm, grey wolf optimisation, whale optimisation algorithm, firefly algorithm, artificial bee colony, ant lion optimisation, harris hawks optimisation, and sine cosine optimisation algorithm. The results are discussed in the respective sections with a focus on the convergence behaviour of the algorithms.