Author : Nafiseh Naji

This study is conducted with the aim of introducing metaheuristic algorithms with emphasis on particle swarm optimization, firefly, and genetic algorithms. Metaheuristic algorithms have today are widely usable in different fields of optimization science. The foundation of these algorithms is mainly the order or rules in natural organisms or derived from other branches of science. In this study, among different metaheuristic algorithms, particle swarm optimization, firefly, and genetic algorithms is investigated. Firefly algorithm is a type of metaheuristic algorithm derived from nature and based on Collective Intelligence algorithms on basis of flashing light of fireflies. The algorithm is able to solve problem with all branches of science. The results obtained from this study show that firefly algorithm is simple in terms of concept and implementation and can solve all applied problems. Moreover, it could be mentioned that the algorithm can typically determine achievement to result. Firefly algorithm is one of the best metaheuristic algorithms than two other metaheuristic algorithms because of including features such as high speed convergence, non-sensitive to initial values, flexibility and a high error tolerance.

Keywords: Metaheuristic algorithm, particle swarm optimization algorithm, firefly algorithm, genetic algorithm

Article published in International Journal of Current Engineering and Technology, Vol.7, No.3 (June-2017)