基于改进的粒子滤波非线性约束优化算法

Particle filter based on nonlinear constraint optimization algorithms

传统的非线性约束优化算法的精度较低,为了克服这一问题,提出了一种基于粒子滤波的新型优化算法。该算法用于解决非线性约束优化问题,并结合粒子滤波器的模型和机制。首先,利用粒子滤波算法的基本原理建立这种优化算法,并给出算法的操作步骤;然后将非线性约束优化问题转换为函数优化问题函数优化问题,并针对非线性约束优化问题,建立粒子滤波优化算法的数学模型。仿真实验结果证明了这种新型算法的正确性,并且表明了相对于传统的优化算法,基于粒子滤波器的优化方法在解决非线性优化问题方面具有更高的效率和速率,并对今后的非线性约束优化问题具有适应性。

In order to overcome the problem of low solution precision of traditional nonlinear constraint algorithms,this paper-proposed a new optimization algorithm based on particle filter.It used this algorithm to solve nonlinear constraint problem,and combined the model and mechanism of particle filters.It established the optimization algorithm based on particle filter algo-rithm,and gave the procedures of which as well.Then it converted the nonlinear constraint optimization problems to function optimization problems,and established mathematical models of particle filter optimization algorithm for the nonlinear constraint optimization problems.Simulation results verify the validity of the new algorithm,and show that the optimization method based on particle filter has higher efficiency and rate than the traditional optimization algorithm when solving nonlinear optimization problems.Meanwhile,the proposed algorithm has its feasibility to future research on nonlinear constraint optimization prob-lems.