基于量子遗传算法的多任务联盟并行生成算法  

Multi-task coalition parallel generation algorithm based on quantum genetic algorithm

作　　者：许波[1] 余建平[2] 

机构地区：[1]茂名学院计算机科学与技术系,广东茂名525000 [2]湖南师范大学数学与计算机科学学院,长沙410081

出　　处：《计算机应用研究》2010年第6期2100-2102,共3页Application Research of Computers

摘　　要：提出一种基于量子遗传算法的多任务联盟并行生成算法,运用量子编码映射的方式将任务分配与资源组合合并为一个过程,使多任务联盟问题的复杂性得到降低。实验表明,该算法在面向多任务的领域中可以快速、有效地并行形成多个任务求解联盟;与遗传算法和蚁群算法的对比实验表明,该算法是正确、有效、可行的,在运行时间和解的性能上都优于前两种算法。This paper presented multi-task coalition parallel generation algorithm based on quantum genetic algorithm, using the quantum encoding map, combined the mix of resources and distribution of tasks into one process, reduced the complexity of the multi-task coalition problem. Experiments show that the algorithm-oriented areas of multi-tasking can be quickly and effectively to solve multiple tasks in parallel to form coalitions. Ant colony algorithm and genetic algorithm and comparison of experiments show that the algorithm is correct, effective and feasible and in the run-time performance of reconciliation are better than the first two algorithms.

关 键 词：多任务联盟 量子遗传算法 多AGENT系统 AGENT联盟 组合优化 

分 类 号：TP393[自动化与计算机技术—计算机应用技术] TP301.6[自动化与计算机技术—计算机科学与技术]