基本粒子群算法和遗传算法用于航路规划的比较

Comparison between Particle Swarm Optimization and Genetic Algorithm for Route Planning

优化问题有两个主要问题:一是要求寻找全局最小点,二是要求有较高的收敛速度。遗传算法和粒子群算法作为启发式算法和群智能算法,因其良好的搜索性能而在飞行器航路规划中得到了广泛的应用。分析了两种算法各自的特点和相互之间的异同点,并在给定相同的作战环境和威胁空间条件下分别进行了航路规划仿真实验,实验结果表明基本粒子群算法在搜索速度和收敛性上均优于基本遗传算法。

There are two primary matters for optimization question one of which is looking for the entire minimal point and the other is speedy convergence velocity. As heuristic and intelligent algorithm, the genetic algorithm and particle swarm optimization are widely used in route planning for their favorable search capability. The article analyse each characteristic and mutual similarities and differences for the two algorithm and simulation is prosecuted in same campaign environment and threat space, which indicate that the particle swarm optimization is excelled than genetic algorithm in search velocity and convergence capability.