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粒子群优化算法(PSO,Particle Swarm Optimization)在空空导弹μ综合控制器参数优化中易出现早熟现象而无法获得全局最优解.针对此问题,提出一种动态加速常数的粒子群优化算法(CPSO,Constant Particle Swarm Optimization).改进算法通过对加速常数的指数形式变化,在寻优前期扩大搜索范围,在后期提高收敛效率,从而避免了寻优过程中的早熟现象.仿真结果表明,改进的CPSO优化算法具有更强的全局搜索能力,设计出的μ综合控制器具有更优的性能,满足给定的性能指标和自动设计指标,节省了大量设计时间,具有工程应用价值.
Particle Swarm Optimization (PSO) is prone to premature phenomenon in parameter optimization of air-to-air missile μ-integrated controller and can not obtain global optimal solution. To solve this problem, a particle swarm optimization algorithm with dynamic acceleration constant (CPSO , Constant Particle Swarm Optimization) .According to the exponential change of acceleration constant, the improved algorithm expands the search range in the early stage of optimization and improves the convergence efficiency in the later stage, thus avoiding the premature phenomenon in the optimization process.The simulation results show that the improved CPSO The optimization algorithm has stronger global search capability. The designed μ integrated controller has better performance, meets the given performance index and automatic design index, saves a large amount of design time and has engineering application value.