Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms
Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms
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DOI：
10.1016/j.cor.2005.02.039
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发表时间：
2006-11-01
影响因子：
4.6
通讯作者：
Passino, KM
中科院分区：
文献类型：
--
作者：
Shima, T;Rasmussen, SJ;Passino, KM
将协同无人飞行器在多目标上执行多任务的分配问题作为一个新的组合优化问题。提出了一种求解这类问题的遗传算法。该算法使我们能够有效地解决经典组合优化方法计算复杂度过高的np困难问题。它还允许我们考虑场景的独特需求，例如任务优先级和协调、时间约束和轨迹限制。遗传算法染色体的矩阵表示简化了遗传算子的编码过程和应用。将该算法的性能与确定性分支定界搜索和随机随机搜索方法进行了比较。蒙特卡洛仿真结果表明，该算法能够快速、一致地给出较好的可行解，从而证明了遗传算法的可行性。这使得高维问题的实时实现成为可能。(c) 2005 Elsevier Ltd版权所有。
A problem of assigning cooperating uninhabited aerial vehicles to perform multiple tasks on multiple targets is posed as a new combinatorial optimization problem. A genetic algorithm for solving such a problem is proposed. The algorithm allows us to efficiently solve this NP-hard problem that has prohibitive computational complexity for classical combinatorial optimization methods. It also allows us to take into account the unique requirements of the scenario such as task precedence and coordination, timing constraints, and trajectory limitations. A matrix representation of the genetic algorithm chromosomes simplifies the encoding process and the application of the genetic operators. The performance of the algorithm is compared to that of deterministic branch and bound search and stochastic random search methods. Monte Carlo simulations demonstrate the viability of the genetic algorithm by showing that it consistently and quickly provides good feasible solutions. This makes the real time implementation for high-dimensional problems feasible. (c) 2005 Elsevier Ltd. All rights reserved.