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微粒群优化算法是一种进化计算技术,其优点是速度快,鲁棒性好且结构简单。针对标准粒子群优化算法容易陷入局部最优的问题,本文提出了一种新的改进粒子群算法,算法中引入了进化速度因子和聚集度因子。在每次迭代时可根据当前粒子群进化速度因子和聚集度因子动态地改变惯性权值,从而使算法具有动态自适应性。将该算法运用到控制分配系统中来获取控制参数。仿真结果表明,该改进算法能得到较好的控制指令来有效的解决战斗机在线故障问题。
Particle swarm optimization algorithm is an evolutionary computing technology, its advantages are fast, robust and simple structure. In order to solve the problem that the standard Particle Swarm Optimization (PSO) is easy to fall into local optimum, a new improved PSO is proposed in this paper. The evolution speed factor and aggregation degree factor are introduced into the algorithm. At each iteration, the inertia weight can be dynamically changed according to the current evolution rate and agglomeration factor of particle swarm optimization, so that the algorithm has dynamic adaptability. The algorithm is applied to the control distribution system to obtain the control parameters. The simulation results show that the improved algorithm can get better control instructions to effectively solve the online fault problem of fighter aircraft.