基于混合PSO算法的加速度计快速标定

针对粒子群优化算法(PSO)在加速度计标定中存在早熟及陷入局部最优的不足,提出了基于差分进化(DE)的双种群信息共享及并行进化的混合PSO算法,并将该算法应用于加速度计快速标定。为提高混合算法的优化性能,提出了一种平衡DE算法全局探索和局部开发能力的加权变异算子,将Logistic函数的非线性特性引入到PSO算法惯性权重和DE算法加权系数的动态调整中。基准测试函数仿真表明所提出的混合算法在收敛速度、收敛精度、全局搜索性能和鲁棒性等方面明显优于PSO、DE算法;加速度计标定仿真结果表明,提出的混合算法能有效提高加速度计的标定精度。

Rapid Calibration of Accelerometer Based on Hybrid Particle Swarm Optimization Algorithm

To overcome the insufficiency of premature and trapped in a local optimum which existed in the calibration optimization of accelerometer based on particle swarm optimization(PSO),a hybrid PSO algorithm based on differential evolution was proposed by dual populations parallel evolutionary and information sharing. It was applied to rapid calibration of accelerometer. In order to improve the optimization ability of hybrid algorithm, a weighted mutation operator was proposed which could balance global exploration and local development ability of DE algorithm, and the nonlinear properties of the Logistic function was introduced to dynamically adjust the inertia weight of PSO algorithm and the weighted coefficient of DE algorithm. Benchmark functions simulation showed that the proposed hybrid algorithm in convergence speed, convergence precision, global search performance and robustness were better than PSO and DE algorithm. And the calibration results of accelerometer showed that the hybrid algorithm could effectively improve the calibration precision of the accelerometer.