空间轨迹问题的粒子群仿真研究

Simulation on Particle Swarm Optimization for Space Locus Problem

空间轨迹的搜索问题具有多个全局最优解,一种有效的解决方法是采用粒子群算法进行搜索。然而与一般的优化问题不同,轨迹问题要求算法中粒子适应值与粒子位置同时收敛。为此,针对已有的粒子群算法在轨迹搜索上的不足,提出了一种减速粒子群优化算法(SlowdownParticleSwarmOptimization,简称SPSO),从位置角度改善粒子群的收敛性能。该算法利用独立子群技术保证粒子收敛于不同的位置,并根据粒子适应值情况减半更新粒子飞行速度,以达到位置收敛的目标。仿真实验的结果表明了减速粒子群算法在位置收敛效果上的优越性。

Searching problem of space locus has multiple optimal solutions. Particle swarm optimization is an effective way to solve locus searching problem. But locus searching requires the convergence of both fitness and position of particle, which is different from general optimal problems. To address this issue, a slowdown particle swarm optimization-SPSO was proposed to improve the convergence performance of particle swarm from the position viewpoint. The particle swarm in SPSO was divided into many independent sub-swarms to guarantee the particles convergent to different position. Furthermore, particle velocity was updated by half according to fitness to achieve the position convergence. The simulation results show the advantage of the proposed slowdown particle swarm optimization-SPSO, which leads to an efficient position convergence.