最优粒子初值有效估计的线阵列方向图优化算法

Linear Array Pattern Optimization Algorithm for Effective Estimation of Optimum Particle Initial Values

粒子群优化算法(PSO)通常随机赋初值。提出一种新的线阵方向图优化的PSO算法。通过矩阵运算解析出对应预期方向图的一组阵元权系数的估值。将该估值视为最优粒子初值的有效估计量。将该估计量赋值给种群的一个粒子,而其他粒子仍然赋随机初值。新优化算法与传统PSO算法唯一区别在于粒子初值的初始化方法。仿真实验结果表明,新算法不但收敛速度更快,而且适应度值收敛的更深,能够有效提高传统PSO算法的在复杂的非线性优化问题上的收敛特性。

For particle swarm optimization （PSO）, all particles are always initialized randomly, but here a new initialization method is presented to improve PSO optimizer convergence performance. On the basis of desired pattern, the corresponding aperture weights are solved by analytical techniques which can to a great extent ensure that these weights are effective estimations of the current optimum particle initial values. Then they are assigned to a particle as initial values, but all other particles of the swarm are still initialized randomly. Except this new initialization step, nothing is changed in PSO optimizer. The simulation results prove that this new optimizer converges faster and the fitness value converges deeper than the standard PSO especially in more complicated optimization problems. So the presented initialization method and new optimizer are effective in improving standard PSO convergence performances.