A Fuzzy Multi-objective Genetic Algorithm for QoS-based Cloud Service Composition

Currently, more and more services are built on the cloud platform, in the area of QoS-based Cloud services composition, some QoS attributes and user preferences are not suitable for accurate representation. This paper introduces the fuzzy sets theory into QoS-based Cloud services composition to solve the above difficulties, and uses triangular fuzzy number to describe the uncertain information. Then the fuzzy QoS total goals are calculated based on weighted-sum approach. Based on the new approach of the fuzzy number comparison, the Pareto dominance relationship is redesigned, the single objective optimization problem is converted to multi-objective optimization problem, and the fuzzy multi-objective genetic algorithm (FMOGA) is designed to obtain Pareto optimal solution set. This method can not only obtain the optimal solutions which are closer to the actual situation, but also solve the question that the multiple attribute decision making methods can not global optimize numerous candidate services. At last, experiments verify the effectiveness and advantage of the proposed method.