Abstract Through the method of multi-objective and multi-attribute decision, this paper solves the problem of QoS (quality of service)-oriented large scale Web service selection and composition. It differs from the traditional method which aggregates all QoS values into a single value. This paper treats all QoS attributes simultaneously by using the multi-attribute decision making method and employs a multi-objective optimization model to formulate the distance of each solution from the positive ideal solution and the negative ideal solution. We develop an ε-dominance multi-objective genetic algorithm to solve the problem of Web service composition optimization. The Pareto frontier, the set of optimal compromise solutions, supports users of either making a flexible decision or choosing an alternative when current service fails. Experimental results verify that the algorithm has satisfying convergence, distribution, and scalability and its computing complexity surpasses the popular non-dominated sorting genetic algorithm (NSGA-II) and strength Pareto evolutionary algorithm 2 (SPEA2).
LU Chenghua,KOU Jisong. Optimization of Large Scale QoS-Oriented Web Service Composition Based on Multi-Objective and Multi-Attribute Decision Making[J]. Chinese Journal of Management, 2018, 15(4): 586-.
