Service Selection of Network Simulation Task Community Based on Improved Particle Swarm Optimization Algorithm

doi:10.3969/j.issn.1000-1093.2012.11.019

Abstract

Abstract: As one of new application requirements in network simulation, to dynamically integrate the distributed various services in network to form a new simulation task community (STC) which meets the needs of different users has become current research focus. This paper presents a simulation service selection method based on the particle swarm optimization. The traditional particle swarm algorithm has some shortcomings that may easily fall into local optima and have slow convergence rate. We design a dynamic inertia weight strategy and a selectable method of mutation. The algorithm can improve the convergence speed not only, but also avoid falling into local optimum. Finally, some typical functions are chosen to test the algorithm. And the results show that the algorithm can select services feasibly and effectively for STC.

Key words: computer application, network simulation, task community, service selection, particle swarm optimization algorithm

CLC Number:

N945.15

SUN Li-yang, LIN Jian-ning, MAO Shao-jie, LIU Zhong. Service Selection of Network Simulation Task Community Based on Improved Particle Swarm Optimization Algorithm[J]. Acta Armamentarii, 2012, 33(11): 1393-1403.

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