Research Of Workflow Service Aggregation Based On Hybrid Multi-objective Particle Swarm Optimization

It has become a hot topic to aggregate multiple workflow services into one with complex function to meet the needs of users. As a result of the number of workflow service increasing, the service aggregation often has a lot of options, but users expect to get the workflow service process meeting the Qos global optimum. However, most existing service aggregates are based on local principle, so it can not meet the needs of the Qos global optimum.The main purpose of this paper is to transform the workflow dynamic service aggregation with Qos global optimum into multi-objective optimization problem with constrained. According to the advantages of solving multi-objective optimization problems by particle swarm optimization, this paper proposes a hybrid multi-objective particle swarm optimization (IHMOPSO). The algorithm, which includes the crossover and mutation strategies from genetic algorithm and selects mechanism which is through the adaptive inertia weight regulation and the probability of global optimum based on crowding distance, improves the defect on the slow convergence and easily falling into local optimal of the multi-objective particle swarm optimization.The main contents of this paper can be summarized as follows:①Transform the Qos global optimum dynamic service aggregation into multi-objective optimization problem with constrained on the basis of studying the workflow Dynamic Service Aggregation.②Through the analysis of critical theory on multi-objective particle swarm optimization, to solve the main problem, an improved hybrid multi-objective particle swarm optimization algorithm is proposed, which uses the strategy of crossover and mutation in genetic algorithm to cross and mutate the individuals in the elite population, which adopts the probability selection mechanism of global optimum based on crowding distance to ensure the diversity of Pareto optimal set, which sets adaptive weight to ensure the balance between global search and local search, which divides population into the elite and general ones to ensure the convergence rate.③Constructs the multi-objective optimization model of workflow service aggregate with Qos, and adopts the improved hybrid multi-objective particle swarm optimization algorithm to solve the multi-objective optimization problem.④This paper constructs a model of work-flow service aggregation combined with the office automation system of XiangHong, and adopts IHMOPSO to solve the multi-objective optimization problem of work-flow service aggregate. It proved to be feasibility with the analysis of convergence speed and the distribution of solution set, and validity compared the experimental results with similar methods.Trough analyzing the result of the project, it proves the algorithm can converge to a set of the aggregation process meeting Qos global optimum, and has better convergence speed and population diversity.