基于鲁棒多目标优化的智能体群组协同任务规划

为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，建立了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，提出了可直接求解模型的区间型非支配排序算法。算法定义了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。

Cooperative Task Scheduling Method for Agent Group using Robust Multi-objective Optimization Approach

To solve the cooperative task scheduling problem under uncertain conditions for agent group, an interval programming mathematical model was established from the aspect of improving robustness of task allocation scheme. A non-dominated sorting algorithm in interval pattern was proposed for solving this model. Since the model aimed to minimize task completion time while maximize task completion quality, dominance relationship between different individuals was defined. Then the algorithm guided the evolution of population by using random genetic operator and heuristics operator in coding space and removed chromosomes to keep the size of population by adopting circular crowded sorting strategy in decoding space. Experiment results show that the proposed method is feasible and effective and can obtain robust and superior task allocation schemes under uncertain conditions.