Using improved firefly algorithm based on genetic algorithm crossover operator for solving optimization problems

• Published in: Journal of intelligent & fuzzy systems 2019-01, Vol.36 (2), p.1547-1562
• Main Authors: Wahid, Fazli, Alsaedi, Ahmed Khalaf Zager, Ghazali, Rozaida
• Format: Article
• Language: English
• Subjects: Ant colony optimization; Convergence; Crossovers; Exploitation; Fitness; Genetic algorithms; Heuristic methods; Light emission; Luminous intensity; Operators (mathematics); Swarm intelligence
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-181936

Firefly algorithm (FA) is one of the most recently introduced stochastic, nature-inspired, meta-heuristic approaches that have seen countless applications in solving various types of optimization problems. The major source of inspiration leading to the development of FA is the phenomenon of light emission by fireflies that attract other fireflies for their potential mates. All the fireflies are unisexual and attract each other according to the intensities of their flash lights. Higher the flash light intensity, higher is the power of attraction and vice versa. For solving optimization problem, the brightness of flash is associated with the fitness function to be optimized. The firefly algorithm is advantageous over other optimization algorithms due to its flexibility, simplicity, robustness and easy implementation but a major drawback associated with the standard FA applied for solving different optimization problems is poor exploitation capability when the randomization factor is taken large during firefly changing position. This poor exploitation may lead to skip the most optimal solution even present in the vicinities of the current solution which results in poor local convergence rate that ultimately degrades the solution quality. To overcome this problem, the crossover operator of genetic algorithm (GA) is incorporated into firefly position changing stage that results in better exploitation capability which improves the local convergence rate resulting in better solution quality. The performance of the proposed approach has been compared with standard FA, GA, artificial bee colony (ABC) and ant colony optimization (ACO) algorithms in terms of convergence rate for various types of minimization and maximization optimization functions.