一种基于改进粒子群算法的机载多传感器任务分配方法
An Airborne Multi-Sensor Task Allocation Method Based on Improved Particle Swarm Optimization Algorithm

分析了机载多传感器任务分配问题的特点，建立了机载多传感器任务分配模型。为解决传统粒子群算法存在的局部收敛、收敛较慢等问题，在现有的粒子群算法基础上，调整算法结构与参数，引入方向系数和远离因子来控制粒子远离最劣解的速度和方向，使其在向最优解移动的同时远离最劣解；基于改进后的粒子群算法提出了一种以最大探测概率为目标函数的机载多传感器任务分配方法，并进行了算法仿真。仿真结果表明，算法可以进行有效的任务分配，并能够提升分配效果。
The characteristics of airborne multi-sensor task allocation problem are analyzed, and an airborne multi-sensor task allocation model is established. In order to solve the problems of local convergence and slow convergence of the traditional Particle Swarm Optimization (PSO) algorithm, the structure and parameters of the existing Particle Swarm Optimization algorithm are adjusted, and the direction coefficient and far away factor are introduced to control the velocity and direction of the particle far away from the worst solution, so that the particle moves away from the worst solution while moving to the optimal solution. Based on the improved Particle Swarm Optimization algorithm, an airborne multi-sensor task allocation method is proposed using maximum detection probability as objective function, and the algorithm is simulated. The simulation results show that this algorithm can effectively allocate tasks and improve allocation effects