Abstract
In this paper, a new and an effective combination of two metaheuristic algorithms, namely Firefly Algorithm and the Differential evolution, has been proposed. This hybridization called as HFADE, consists of two phases of Differential Evolution (DE) and Firefly Algorithm (FA). Firefly algorithm is the nature- inspired algorithm which has its roots in the light intensity attraction process of firefly in the nature. Differential evolution is an Evolutionary Algorithm that uses the evolutionary operators like selection, recombination and mutation. FA and DE together are effective and powerful algorithms but FA algorithm depends on random directions for search which led into retardation in finding the best solution and DE needs more iteration to find proper solution. As a result, this proposed method has been designed to cover each algorithm deficiencies 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 findings showed that HFADE is a more preferable and effective method in solving the high-dimensional functions
Highlights
One of the affective methods in finding the best solution in numerical problems is the Optimization technique
1) Complexity analysis HFADE algorithm is consists of 2 main parts and these parts are executed in parallel form in each cycle which are showed at the flow chart Fig. 1
The Firefly Algorithm (FA) is, arguably, one of the most efficient natureinspired metaheuristic algorithms, which has outperformed most of the algorithms in solving the various optimizing numerical problems
Summary
Abstract—In this paper, a new and an effective combination of two metaheuristic algorithms, namely Firefly Algorithm and the Differential evolution, has been proposed. FA and DE together are effective and powerful algorithms but FA algorithm depends on random directions for search which led into retardation in finding the best solution and DE needs more iteration to find proper solution. As a result, this proposed method has been designed to cover each algorithm deficiencies so as to make them more suitable for optimization in real world domain.
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