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

Abstract

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 natureinspired 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 highdimensional functions.

Hybrid Differential Evolution Based Probabilistic Model to Support Quality Aware Best Path Routing Scheme for Mobile Adhoc Networks

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Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach

“…In last two decades researchers have started applying the meta-heuristic search algorithms [40,[43][44][45] to the field of model-based testing [22,31,34,39,78] for generating optimized test cases or test sequences. Nature inspired algorithms are gradually being hybridised [86,87] keeping in mind the best features of different algorithms, [1,18,24,32,48,63] to obtain more variation and quality in the solutions, as some metaheuristics are very efficient in exploration where as others are good at exploitation. The hybrid metaheuristic techniques are created by combining two search algorithms where balance between exploration and exploitation is maintained, one algorithm is better in exploration and other one in exploitation [86].…”

A new fusion of salp swarm with sine cosine for optimization of non-linear functions

“…Hybridization of nature inspired algorithms is a popular approach for to merge merits and strength of standalone algorithms for handling those deficiencies [9]. Several typical studies can be seen in [21,38,42,44,47,50,61,82,83] in which the hybrid algorithms merging advantages of single ones performed well in boosting the accuracy of functions and reducing classification time. As an example, Sarbazfard et al [42] developed a hybrid variant called HFADE that integrates differential evolution (DE) with Firefly algorithm (FA) for improving exploration tendency of those algorithms.…”

“…Several typical studies can be seen in [21,38,42,44,47,50,61,82,83] in which the hybrid algorithms merging advantages of single ones performed well in boosting the accuracy of functions and reducing classification time. As an example, Sarbazfard et al [42] developed a hybrid variant called HFADE that integrates differential evolution (DE) with Firefly algorithm (FA) for improving exploration tendency of those algorithms. Firefly algorithm and differential evolution both are effective techniques but firefly approach depends on arbitrary instructions for hunt, which lead into retardation in searching the superior and possible global result in the search area.…”