基于改进PSO算法的导弹控制参数优化

研究了将粒子群算法(PSO)应用于空对空导弹控制参数自动设计的方法,解决导弹控制参数手工设计中遇到的困难与问题.标准PSO算法在导弹静稳定工作点参数优化中表现出良好性能,但在静不稳定工作点优化时容易限入局部最优,因此引入遗传算法(GA)的杂交思想对标准PSO算法进行了改进,以扩大解空间的范围.仿真结果表明:改进后的PSO优化算法具有更强的全局搜索能力,获得的参数能够满足给定的性能指标,并且可以节省大量的设计时间,具有很高的工程应用价值.

Missile Control Parameter Optimization Based on Improved PSO Algorithm

To solve the problem in air-to-air missile control parameter design,an automatic control parameter optimization technique is discussed based on particle swarm optimization(PSO) algorithm.The result shows that the standard PSO has good performance at statically stable points of the missile,but at statically unstable points it has a local extreme value in most cases.For expanding the diversity of particles,the hybrid method is taken from genetic algorithm(GA),so that the overall searching ability is enhanced,...