中文摘要：

为解决具有不确定时间约束的网格服务流程编排问题，提出了一种基于模糊集的智能优化技术．建立了基于模糊集的服务时间模型，并对不确定的服务时间和用户期望完成时间进行形式化描述．该模型求解属于NP难题，因此在标准遗传算法（GA）中引入了自适应混沌控制策略．分析和模拟实验结果表明，混沌特性能引导GA快速收敛并避免局部最小解．根据改进的基于熵的性能评价策略，该方法的收敛速度和稳定性均优于标准GA．

英文摘要：

A new method of intelligent optimization to solve grid service composition problem with uncertain time constraints based on fuzzy set is proposed. A fuzzy service time （FST） model is adopted to formalize the uncertain service time and deadline of user. A self-adaptive chaotic control strategy is incorporated into standard genetic algorithm （GA） because it＇s NP hard to solve FST. Analytical and experimental results showed that the convergence and prematurity of GA may be improved prominently by chaos. According to the improved entropy based performance evaluation strategy, the efficiency and stability of the resulting algorithm outperform the standard GA.