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为了解决不确定性条件下的智能体群组协同任务规划问题,从提高任务分配方案鲁棒性的角度出发,建立以最小化任务完成时间和最大化任务完成质量为目标的区间规划模型,提出可直接求解模型的区间型非支配排序算法.算法定义区间目标函数间的占优支配关系,在编码空间通过组合使用随机遗传算子和启发式算子引导种群进化,在解码空间采用循环拥挤距离排序淘汰染色体保持种群规模.实验结果表明,所提出的方法可行有效,在不确定性条件下能得到鲁棒优质的任务分配方案.
In order to solve the problem of collaborative mission planning for agent groups under uncertain conditions, an interval planning model is proposed to minimize the task completion time and maximize the task completion quality, from the viewpoint of improving the robustness of task assignment scheme. The algorithm can directly solve the model of interval-type non-dominated sorting algorithm.The algorithm defines the dominance relationship between the objective function in the interval, guides the population evolution by combining the use of stochastic genetic operators and heuristic operators in the code space, Sorting out chromosomes to keep the population size.The experimental results show that the proposed method is feasible and effective and can obtain a robust and high-quality task allocation scheme under the condition of uncertainty.