Abstract:

To solve the problem of Web services selection with quality of service （QoS） global optimization, a novel dual population co-evolutionary Web services selection algorithm is proposed. Inspired by the improved nondominated sorting genetic algorithm (NSGA-II) and based on multi-objective discrete particle swarm optimization algorithm, the proposed algorithm uses a dual population co-evolutionary framework to do non-dominated sorting and elitist maintaining synchronously, and defines a new discrete particle position update operator. Furthermore, a distance-based diversity measurement operator, an adaptive value aware particle selection operator and a roulette-based global optimal solution selection strategy are designed to ensure particles diversity and achieve better global convergence ability. The proposed algorithm can optimize multiple objectives simultaneously and finally obtain a set of constrained Pareto optimum composite service solutions. Theoretical analysis and experimental results indicate that the proposed algorithm not only owns satisfied performance and robustness, but also has better solution quality and distribution than NSGA-II, all of which indicate that the proposed algorithm is an efficient method applied to QoSaware Web service selection with global optimization.