Paper Title :A New Hybrid Firefly with Genetic Algorithm for Global Optimization

Author :Mariam Elkhechafi, Hanaa Hachimi, Youssfi Elkettani

Article Citation :Mariam Elkhechafi ,Hanaa Hachimi ,Youssfi Elkettani , (2017 ) " A New Hybrid Firefly with Genetic Algorithm for Global Optimization " , International Journal of Management and Applied Science (IJMAS) , pp. 47-51, Volume-3,Issue-4

Abstract : Because of its nonlinearity and multimodality, global optimization is often too difficult to solve. This is why the traditional algorithm still limited to this challenge. In this paper, we present a new hybrid algorithm which is a combination of a Genetic algorithms (GA) and Firefly algorithm (FA). We focus in this research on a hybrid method combining two heuristic optimization techniques (GA) and Firefly algorithm (FA) for the global optimization. Denoted as GA-FA. This hybrid technique incorporates concepts from GA and FA and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of FA. In order to test the performance of the proposed approach a diverse set of selected benchmark functions are employed. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA) and Genetic algorithms (GA). Keywords- Optimization, Metaheuristics, Hybrid Algorithms, Genetic algorithm, Firefly algorithm.

Type : Research paper

Published : Volume-3,Issue-4

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