面向QoS全局优化的大规模Web服务组合方法

QoS-oriented global optimization approach for mass Web services composition

把多个简单Web服务组合成为更强大的组合Web服务是面向服务计算的目标之一。由于存在多个功能相同但服务质量属性不同的候选Web服务,因此需要针对服务质量要求进行服务组合。鉴于Web服务组合规模的不断增长和特定领域的时限要求,面向实时大规模Web服务组合问题的快速收敛算法尤为重要,然而目前相关工作还很少。论文提出一种新的Web服务组合算法GAELS(Genetic Algorithm Embedded Local Searching),运用高适应度初始种群和局部搜索的变异策略,加快收敛速度。通过实验评测表明与简单遗传算法相比,GAELS算法能更快得到近似最优解,且随着服务规模增长,拥有更好的适应性。

One of the aims of SOA is to compose atomic Web services into a powerful composite service.QoS based selection approaches are used to choose the best solution among candidate services with the same functionality.Due to the increasing scale of the candidate Web services and real-time demands of specific application domain,the rapid convergent algorithm for mass Web services composition is special important.However,rare work has been done to solve the problem.The paper proposes a new algorithm named GAELS（Genetic Algorithm Embedded Local Searching）,which uses the strategies of high-fitness initial population and mutation with local searching to speed the convergence.The in-depth experimental results show that the proposed GAELS algorithm can get the approximately optimal solution more quickly and be more adaptive to the expanding of candidate services than simple genetic algorithm.