A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition
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.
KeywordsService composition Cloud computing Quality of service (QoS) Genetic algorithm Fruit fly optimization algorithm
- Alrifai, M., & Risse, T. (2009). Combining global optimization with local selection for efficient QoS-aware service composition. In: International World Wide Web Conference Committee, Madrid, Spain (pp. 881–890).Google Scholar
- Alrifai, M., Skoutas, D., & Risse, T. (2010). Selecting skyline services for QoS-based web service composition. In: International World Wide Web Conference Committee, 2010, Raleigh, North Carolina, USA.Google Scholar
- Canfora, G., Di Penta, M., Esposito, R., & Villani, M. L. (2005). An approach for QoS-aware service composition based on genetic algorithms. In: Proceedings of the Conference on Genetic and Evolutionary Computation (pp. 1069–1075). Berlin: Springer.Google Scholar
- El Haddad, J., Manouvrier, M., & Rukoz, M. (2010). TQoS: Transactional and QoS-aware selection algorithm for automatic web service composition. IEEE Transactions on Services Computing, 3(1), 73–85.Google Scholar
- Goldberg, D. E. (1989). Genetic algorithms in search, optimization & machine learning. Reading, MA: Addison-Wesley.Google Scholar
- Jin, H., Yao, X., & Chen, Y. (2015). Correlation-aware QoS modeling and manufacturing cloud service composition. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-015-1080-2.
- Montgomery, D. C. (2005). Design and analysis of experiments. Arizona: Wiley.Google Scholar
- Mousavi, S. M., Alikar, N., Niaki, S. T. A., & Bahreininejad, A. (2015b). Optimizing a location allocation-inventory problem in a two-echelon supply chain network: A modified fruit fly optimization algorithm. Computers and Industrial Engineering, 87, 543–560.Google Scholar
- Tang, M., & Ai, L. (2010). A hybrid genetic algorithm for the optimal constrained web service selection problem in web service composition. In: Proceeding of the 2010 world congress on computational intelligence (pp. 1–8). Barcelona.Google Scholar
- Zeleny, M. (1982). Multiple criteria decision making. New York: McGraw-Hill.Google Scholar
- Zhang, Y., Cui, G., Wang, Y., Guo, X., & Zhao, Shu. (2015). An optimization algorithm for service composition based on an improved FOA. Tsinghua Science and Technology, 20(1), 90–99.Google Scholar
- Zhao, J., & Xiaofang, Y. (2015). Multi-objective optimization of stand-alone hybrid PV-wind-diesel-battery system using improved fruit fly optimization algorithm. Soft Computing. doi: 10.1007/s00500-015-1685-6