A New Hybrid Firefly with Genetic Algorithm for Global Optimization
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.