中文摘要：

为了研究时间约束下的高层次数据流调度问题,提出了遗传算法和蚂蚁算法动态融合的解决方案.给出了时间约束调度中遗传算法的编码方法、交叉、变异和适应度函数以及蚂蚁算法中的概率选择方法和信息素的更新规则.为了找到遗传算法与蚂蚁算法的最佳切换时机,还解决了2个关键问题：遗传算法的动态结束条件和蚂蚁算法中初始信息素的产生.实验结果表明,该方法所用的平均资源数目比遗传算法少5.2%,比蚂蚁算法少4.9%;运行时间比遗传算法少44%,比蚂蚁算法少31%.

英文摘要：

In order to resolve problems inherit in high level data flow scheduling with time constrains, a method dynamically combining a genetic algorithm （GA） and an ant algorithm （AA） was developed. Encoding methods, crossovers, mutations, and the fitness function of the GA were evaluated, as well as probability selections and pheromone update rules for the AA. To determine the optimal opportunity for a switch from GA to AA, two critical problems had to be resolved： the first was a means to dynamically determine termination conditions for the GA; the second was a method for using the scheduling results of the GA to generate the initial pheromone distribution of the AA. Experimental results showed that, on average, the resources needed in the proposed method were 5.2% less than with GA alone and 4.9% less than with AA alone. The run time using the proposed method was 44% less than with GA and 31% less than with AA.