Authors: Fazli Wahid; Rozaida Ghazali
Addresses: Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn, Malaysia ' Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn, Malaysia
Abstract: Firefly algorithm (FA) is a newly developed nature-inspired, metaheuristic, stochastic algorithm that has seen many applications in solving problems of optimisation nature since its introduction just a couple of years ago. FA is a simple, flexible, easily implementable and robust approach inspired from natural phenomenon of light emission by fireflies but a major drawback associated with FA is the random initial solution set generation that degrade the solution quality. In this work, the targeted issue has been resolved by introducing genetic algorithm (GA) operators namely selection, mutation and cross over operators during initial solution set generation for standard FA. The proposed technique has been applied to few standard benchmark minimisation and maximisation functions and the results have been compared with standard FA and GA. A significant amount of improvement in the convergence rate can be observed that results in high quality solution for solving optimisation problems.
Keywords: standard firefly algorithm; genetic algorithm; GA; random solution generation; hybrid GA-FA; faster convergence.
International Journal of Computer Aided Engineering and Technology, 2021 Vol.14 No.1, pp.62 - 79
Received: 01 Dec 2017
Accepted: 01 Jun 2018
Published online: 07 Dec 2020 *