计算机工程Issue(3)：23-27,38,6.DOI:10.3969/j.issn.1000-3428.2014.03.005
基于混沌多目标粒子群优化算法的云服务选择
Cloud Service Selection Based on Chaotic Multi-objective Particle Swarm Optimization Algorithm
摘要
Abstract
With the explosive number growth of services in cloud computing environment, how to select the services that can meet user’s requirement from the services which have same or similar function becomes the key problem to be resolved in cloud computing. So a multi-objective service composition optimization model with Quality of Service(QoS) restriction is built, and since some disadvantages of the traditional Multi-objective Particle Swarm Optimization(MOPSO) algorithm, such as less diversity of solutions and falling into local extremum easily, a method of Chaotic MOPSO(CMOPSO) algorithm is proposed. This algorithm uses the information entropy theory to maintain non-dominated solution set so as to retain the diversity of solution and the uniformity of distribution. When the diversity of population disappears, it introduces chaotic disturbance mechanism to improve the diversity of population and the ability of global optimization algorithm to avoid falling into local extremum. Experimental result shows that the astringency and the diversity of solution set of CMOPSO algorithm are better than traditional MOPSO algorithm, and it can solve the problem of service dynamic selection under cloud computing environment more efficiently.关键词
云计算/服务选择/服务质量/多目标粒子群优化算法/信息熵/混沌Key words
cloud computing/service selection/Quality of Service(QoS)/Multi-objective Particle Swarm Optimization(MOPSO) algorithm/information entropy/chaotic分类
信息技术与安全科学引用本文复制引用
王娜,卫波,王晋东,张恒巍..基于混沌多目标粒子群优化算法的云服务选择[J].计算机工程,2014,(3):23-27,38,6.基金项目
河南省科技攻关计划基金资助项目(122102310003)。 （122102310003）
