一种非线性权重的自适应粒子群优化算法

针对粒子群优化算法中出现早熟和不收敛问题，分析了基本PSO算法参数对其优化性能的影响，提出了基于非线性权重的自适应粒子群优化算法（NWAPSO）。在优化过程中，惯性权重随迭代次数非线性变化，改进的算法能使粒子自适应地改变搜索速度进行搜索，并与基本粒子群算法以及其他改进的粒子群算法进行了比较。实验结果表明，该算法在搜索精度和收敛速度等方面有明显优势。特别对于高维、多峰等复杂非线性优化问题，算法的优越性更明显。

Adaptive particle swarm optimization algorithm of nonlinear inertia weight

Aiming at the prematurity and non-convergence problems in particle swarm optimization algorithmt,he parameters of standard PSO affecting its optimization performance is analysed and an adaptive particle swarm optimization algorithm based on nonlinear inertia weight is pointed out.During optimizationt,he inertia weight changes with nonlinearity along itera-tion timest,he improved algorithm can adaptively change search speed.A compare is made with the standard Particle Swarm Optimization as well as other advanced Particle Swarm Optimization.The experimental results illustrate that the proposed algo-rithm has evident superiorities in search precision and convergence speed.Especiallyt,here are evident superiorities in multi-di-mension and multi-peak nonlinear optimization questions.