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Titre: A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition
Auteur(s): Seghir, Fateh
Khababa, Abdellah
Mots-clés: Service composition
Cloud computing
Quality of service (QoS)
Genetic algorithm
Fruit fly optimization algorithm
Date de publication: 8-aoû-2018
Collection/Numéro: Journal of Intelligent Manufacturing J Intell Manuf;DOI https://doi.org/10.1007/s10845-016-1215-0
Publisher Name Springer US;
Résumé: This paper addresses the QoS-aware cloud service composition problem, which is known as a NP-hard problem, and proposes a hybrid genetic algorithm (HGA) to solve it. The proposed algorithm combines two phases to perform the evolutionary process search, including genetic algorithm phase and fruit fly optimization phase. In genetic algorithm phase, a novel roulette wheel selection operator is proposed to enhance the efficiency and the exploration search. To reduce the computation time and to maintain a balance between the exploration and exploitation abilities of the proposed HGA, the fruit fly optimization phase is incorporated as a local search strategy. In order to speed-up the convergence of the proposed algorithm, the initial population of HGA is created on the basis of a heuristic local selection method, and the elitism strategy is applied in each generation to prevent the loss of the best solutions during the evolutionary process. The parameter settings of our HGA were tuned and calibrated using the taguchi method of design of experiment, and we suggested the optimal values of these parameters. The experimental results show that the proposed algorithm outperforms the simple genetic algorithm, simple fruit fly optimization algorithm, and another recently proposed algorithm (DGABC) in terms of optimality, computation time, convergence speed and feasibility rate.
URI/URL: http://dspace.univ-setif.dz:8888/jspui/handle/123456789/2541
ISSN: Print ISSN 0956-5515
Online ISSN 1572-8145
Collection(s) :Articles

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