S-ABC--面向服务领域的人工蜂群算法范型

摘要

In the service computing field,the typical service optimization problems (such as service selection,service composition and service resource scheduling)become more and more complicated with the rapid development of cloud computing,internet of things and big data.Meanwhile,in many service sectors,the service domain features (such as service priori,correlation and similarity) have been gradually formed with the long-term evolution of service business.These domain features have strong influences on solutions of service optimization problems.If the service domain features are not considered adequately,the service optimization problems can not be solved effectively and efficiently.Therefore,how to design service domain-oriented optimization algorithm paradigm, efficient algorithms and optimization strategies become the critical challenges.This paper analyzes the influences on service optimization problems by service domain features.Then,based on the improved strategies of artificial bee colony algorithm,a service domain-oriented artificial bee colony algorithm paradigm (S-ABC)is presented.The optimization principle of S-ABC paradigm is described in detail.The better optimization results are verified by means of confirmatory experiment. This research work shows a new and better method for solving service optimization problems with the support of service domain features,and to extend the theory of swarm intelligence optimization in service computing field.%服务计算优化问题（如服务选择、服务组合、服务资源调度等）随着云计算、物联网、大数据的快速发展而变得日益复杂。另一方面，各服务行业在其长期演化中逐渐形成了特有的领域特性（如服务先验性、关联性、相似性等）。这些特性对服务优化问题求解有重要影响，如果对其考虑不充分，将导致服务优化问题求解的效率与效果不理想。因此，如何构建面向服务领域的服务优化算法范型及高效求解算法与优化策略成为亟待解决的关键问题。文中分析了服务领域特性对服务优化问题求解的影响规律，据此改进了人工蜂群算法的优化策略，提出了面向服务领域的人工蜂群算法范型（Service domain-oriented Artificial Bee Colony algorithm paradigm，S-ABC），阐述了S-ABC 范型的优化机理，并通过验证实验证实了该算法范型的优化效果。该研究工作为利用服务领域特性指导服务优化问题求解算法的设计提供了新的研究思路和方法，深化了群体智能算法在服务领域的应用，扩展了群体智能算法的优化理论。