A Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Global Optimization

In this paper, a new and an e ective combination of two metaheuristic algorithms, namely Fire y Algorithm and the Di erential evolution, has been proposed. This hybridization called as HFADE, consists of two phases of Di erential Evolution (DE) and Fire y Algorithm (FA). Fire y algorithm is the nature- inspired algorithm which has its roots in the light intensity attraction process of re y in the nature. Di erential evolution is an Evolutionary Algorithm that uses the evolutionary operators like selection, recombination and mutation. FA and DE together are e ective and powerful algorithms but FA algorithm depends on random directions for search which led into retardation in nding the best solution and DE needs more iteration to nd proper solution. As a result, this proposed method has been designed to cover each algorithm de ciencies so as to make them more suitable for optimization in real world domain. To obtain the required results, the experiment on a set of benchmark functions was performed and ndings showed that HFADE is a more preferable and e ective method in solving the high-dimensional functions.