一种支持高效服务选择的混合增强ABC算法

A Hybrid Enhancement Artificial Bee Colony Algorithm for High-efficiency Service Selection

为了高效地实现服务选择,利用服务聚合方法构建最大化满足用户需求的复杂软件服务系统,提出了一种混合增强人工蜂群(hybrid enhancement artificial bee colony,HEABC)算法。该算法将K-means算法、KNN(K-nearest neighbor)算法与ABC算法融合,保证ABC算法在离散解空间更新解时,始终保持连续性。通过增加蜜蜂群体之间信息共享的能力,增强了蜜蜂群体的探索和开发能力。在对软件服务的非功能性感知方面,引入了服务契约的概念,以实现更加全面的满足用户个性化、动态化需求。仿真实验使用了60组不同的数据集,在质量和执行时间方面与其他算法进行了比较。结果表明,与其他算法相比,该算法在求解时间和求解质量上均有所提高。

In order to implement service selection efficiently,and to build a complex software service system that can meet the needs of users by using service aggregation method,this paper proposes a Hybrid Enhancement Artificial Bee Colony(HEABC)algorithm.The algorithm combines K-means algorithm,K-Nearest Neighbor(KNN)algorithm and ABC algorithm to ensure that ABC algorithm always maintains continuity when updating solutions in discrete solution space.The algorithm enhances the exploration and development capabilities of the bee colony by increasing the ability of information sharing between bee colonies.In terms of non-functional perception of software services,this paper introduces the concept of service contract to achieve more comprehensive user satisfaction and dynamic needs.The simulation experiment used 60 different sets of data and compared it to other algorithms in terms of quality and execution time.The results show that compared with other algorithms,this algorithm has improved the solution time and the solution quality.