计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

Web服务组合QoS优化中的改进遗传算法

欧阳超,陈志泊,孙国栋   

  1. (北京林业大学 信息学院,北京 100083)
  • 收稿日期:2016-06-14 出版日期:2017-08-15 发布日期:2017-08-15
  • 作者简介:欧阳超(1991—),男,硕士研究生,主研方向为Web服务、数据库技术;陈志泊(通信作者),教授、博士;孙国栋,副教授、博士。
  • 基金项目:
    中央高校基本科研业务费专项资金(TD2014-01)。

Improved Genetic Algorithm for Web Service Composition QoS Optimization

OUYANG Chao,CHEN Zhibo,SUN Guodong   

  1. (School of Information Science and Technology,Beijing Forestry University,Beijing 100083,China)
  • Received:2016-06-14 Online:2017-08-15 Published:2017-08-15

摘要: 结合模拟退火算法与传统遗传算法,提出一种应用于Web服务组合质量优化的改进遗传算法。在选择算子和变异算子的筛选过程中引入模拟退火算法选择更优解的思想,并在算法选择和变异过程中通过设置过滤劣质基因的概率以及逐渐增加变异比率,保证算法种群的多样性。实验结果表明,与传统遗传算法、模拟退火算法、粒子群优化算法等相比,改进算法的收敛速度更快,并且获取的Web服务组合质量更高。

关键词: Web服务, 服务质量, 遗传算法, 模拟退火算法, 粒子群优化算法, 进化算法

Abstract: An improved Genetic Algorithm(GA) for Web service composition Quality of Service(QoS) optimization is brought up by combining Simulated Annealing(SA) algorithm and traditional GA.In order to keep the diversity of the population,the thought of choosing better solution in SA is introduced into the selection of reproduction operator and mutation operator in GA,and a filter rate to dislodge inferior genes in reproduction and procedures in GA is set.The experiment result shows that compared with traditional GA,SA and Particle Swarm Optimization(PSO) algorithm,the improved GA has a better performance in both Web service composition quality and convergence speed.

Key words: Web service, Quality of Service(QoS), Genetic Algorithm(GA), simulated annealing algorithm, Particle Swarm Optimization(PSO) algorithm, evolutionary algorithm

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