基于C-MMAS算法的组合服务动态选择研究

将大规模的具有多种组合路径的QoS最优组合服务选择转换成带约束的最优路径选择问题，并提出了一种基于文化的最大一最小蚁群优化算法(C-MMAS)来完成最优路径选择。C-MMAS计算模型由基于MMAS的群体空间、基于优秀解的信仰空间及其之间的通信协议组成。群体空间在完成基于MMAS的演化后进行基于“变异”的进化操作，并将每次演化和进化后的优秀解作为知识贡献给信仰空间，信抑空间按照一定的优化规则更新空间里的知识，当信仰空间里的知识经过若干代的积累沉淀后再对群体的演化进行指导。此计算模型在知识和群体层面使用双重进化机制支持问题的求解和知识的提取，充分利用了种群的进化机制和知识的指导作用，在很大程度上提高了种群的多样性及收敛速度，达到了防止早熟、降低计算代价的目的。理论分析和实验结果说明了该算法的可行性和有效性。

Research on Composite Services Selection Based on C-MMAS Algorithm

The problem of composite Web services selection with multiple composite paths was transformed into a constraint optimal path selection problem. A new optimization algorithm C-MMAS was proposed by integrating Max-Min Ant System into Culture algorithm framework, and was applied to solve the optimal path selection problem. This compuling model consists of a MMAS-based population space, excellent solution-based belief space and communication protocols between the two spaces. After completing MMAS-based evolution, population space carrys out variation-based evolution, and contributes excellent solutions as knowledge to belief space after evolutions. Belief space updates knowledge according to certain optimization principle. When the knowledge in belief has been accumulated and precipitated some generations, it is used to guide the MMAS-based evolution. Due to implementing two evolutionary mechanisms on population and knowledge,making the best use of population's evolutionary mechanism and guidance effect of knowledge, this computing model has improved population's diversity and convergence speed largely, realized the purpose of avoiding precocity and reducing computing expense. Theoretical analysis and experimental results indicate the feasibility and efficiency of this algorithm.