应用离散粒子群-郭涛算法分配多无人机协同任务

针对以往考虑时间窗约束的多无人机协同任务分配问题模型不能反映在有效时间窗内,任务执行时间对任务收益的影响及求解算法效率较低的问题。建立了将任务收益和任务执行时间直接联系起来的任务分配模型和可行解到粒子整数编码方式的映射,设计了混合离散粒子群-郭涛算法的组合优化问题求解策略。借助粒子群算法利用粒子自身信息和种群有用信息指导种群进化的本质特点,优化郭涛算法的适应性序列倒置操作;设计了可变的学习选择概率来选择个体的学习粒子,改进了序列倒置算子。仿真实验验证了该方法处理复杂任务分配问题的有效性。

Cooperative task allocation of multi-UAVs with mixed DPSO-GT algorithm

A general mathematics model for cooperative task allocation of multi-UAVs with time windows constrains has proposed which incorporates task gains and execution time directly and simplifies the model formulation and algorithm designing. By defining a suitable particle structure, an algorithm based on the principles of discrete particle swarm optimization and Guo Tao algorithm is designed. The Inver-over Operator is directed by the swarm, the local and global optimal. Variable learning selection probability is introduced into the algorithm to select the learning particles, and the Inver-over Operator is modified. Simulation verifies the proposed task planning methodology for complex missions.