基于改进蚁群优化算法的服务组合与优化方法  

Service Composition and Optimization Method Based on Improved Ant Colony Optimization Algorithm

作　　者：沈记全[1] 罗常委 侯占伟[1] 刘志中[1] SHEN Jiquan;LUO Changwei;HOU Zhanwei;LIU Zhizhong(College of Computer Science and Technology,Henan Polytechnic University,Jiaozuo,Henan 454000,China)

机构地区：[1]河南理工大学计算机科学与技术学院,河南焦作454000

出　　处：《计算机工程》2018年第12期68-73,共6页Computer Engineering

基　　金：国家自然科学基金青年基金(61300124);河南省基础与前沿技术研究计划项目(152300410212);河南省科技攻关计划项目(162102310426;172102310250);河南省教育厅自然科学基金(17A520034)

摘　　要：针对传统蚁群算法存在初期信息素积累时间长、易陷入局部最优等不足,在满足用户全局服务质量约束的条件下,提出一种改进的基于蚁群系统的云服务组合算法。借鉴遗传算法的思想得到蚁群系统的初始信息素分布,通过社会认知优化改进蚂蚁寻优路径,并采取优化的蚁群信息素更新策略,从而提高算法搜索效率。实验结果表明,改进的蚁群优化算法在求解云服务组合问题上具有更优的搜索性能。Aiming at the shortcomings of traditional ant colony algorithm,such as long initial pheromone accumulation time and easy to fall into local optimum,an improved ant colony system based cloud service composition algorithm is proposed under the condition of satisfying users’global Quality of Service(QoS)constraints.The initial pheromone distribution of the ant colony system is obtained by the idea of Genetic Algorithm(GA).The ant optimization path is improved through the learning method of social cognitive optimization algorithm,and the algorithm search efficiency is improved by adopting the optimized ant colony pheromone update strategy.Experimental results show that the improved ant colony optimization algorithm has higher search performance in solving cloud service composition problems.

关 键 词：云服务 全局约束 蚁群系统 遗传算法 社会认知优化算法 服务组合 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]