Ant colony algorithm is applied to solve unmanned aerial vehicle (UAV) cooperative multi-task assignment problem (CMTAP).
采用蚁群算法对无人机协间多任务分配问题(CMTAP)进行研究。
Beyond this point, solution of the task assignment problem requires empirical knowledge of the specific empirical complementarities that exist.
除了这一点，解决任务分配问题还需要对存在的特定的经验互补性的经验知识。
The Particle Swarm Optimization (PSO) algorithm was used to deal with task assignment problem in multi-suppliers' participation in collaborative product development.
通过粒子群最优化算法解决多供应商参与协同产品开发时的任务指派问题。
The processing task assignment is formulated as a quadratic 0-1 programming problem, and a distributed OALL algorithm (optimizing assignment layer by layer) is proposed.
针对处理任务分配，将其建模为二次0 - 1规划问题，并提出了分布式逐层优化分配算法oall。
A kind of time optimal formation strategy is proposed, which is based on path planning and task decomposition as well as an effective algorithm for assignment problem.
针对移动机器人的最速编队问题，结合路径规划和任务分解，提出一种分派问题的新解法和时间最优的编队策略。
A kind of time optimal formation strategy is proposed, which is based on path planning and task decomposition as well as an effective algorithm for assignment problem.
针对移动机器人的最速编队问题，结合路径规划和任务分解，提出一种分派问题的新解法和时间最优的编队策略。
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