Authors: Fateh Seghir Abdellah Khababa
Publish Date: 2016/04/08
Volume: 29, Issue: 8, Pages: 1773-1792
Abstract
This paper addresses the QoSaware cloud service composition problem which is known as a NPhard 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 speedup 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
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