多编组协同任务分配模型及DLS-QGA 算法求解

为解决多智能体编组协同任务分配问题, 定义任务、智能体编组和相关的分配过程变量, 建立以最高任务执行效率为目标的数学模型. 在问题模型中设计考虑资源损耗的编组资源能力更新机制, 提出用于求解该模型的动态列表规划和量子遗传算法的混合任务分配算法, 使用动态列表规划选择处理的任务, 利用量子遗传算法为选定任务分配最合适编组. 最后通过算例表明, 所提出的方法在解决时序逻辑任务分配时能够得到更优更稳定的方案.

Cooperative task allocation methods in multiple groups using DLS-QGA

To solve the cooperative task allocation of multiple agent groups, the task, agents group and process variables are defined. The mathematical model in pursuit of maximal task execute efficiency is established. The wastage of group resource capability is introduced in the process of problem modeling. A hybrid approach to the problem based on dynamic list scheduling(DLS) and the quantum genetic algorithm(QGA) is proposed. In the DLS-QGA algorithm, DLS is used to select the task needed to dispose, then QGA is used to select the best group for the selected task. Finally, the superiority and applicability of this approach are illuminated by the simulation of campaign assumption, and the scheme can be better and more steadily in task allocation with time-logic.