论文部分内容阅读
针对粒子群算法在解决非线性类的误差评定中遇到收敛慢,易陷入局部最优的问题,且一般所提出的形状误差计算方法均针对特定模型的误差计算,通过对速度和搜索范围的相对调整,提出一种基于可变搜索区域的自适应粒子群优化算法.并将其用于球体、圆柱、圆锥的误差评定.对三类误差评定模型算法框架进行了统一描述,并给出各改进策略的实现要点.对比实验的计算结果表明,该方法对上述三类误差评定问题均有较好效果,在解决类似非线性优化问题时,其计算精度优于其他同类算法.
In order to solve the problem of non-linear class errors, Particle Swarm Optimization (PSO) encounters the problem of slow convergence and easy to fall into the local optimum. Generally, the calculation methods of shape errors are aimed at calculating the error of a specific model. Relative adjustment, an adaptive particle swarm optimization algorithm based on variable search area is proposed, which is used to evaluate the error of sphere, cylinder and conic.Three kinds of algorithm framework of error evaluation model are described uniformly, The results show that the proposed method is effective for the above three kinds of error evaluation problems, and its computational accuracy is superior to other similar algorithms in solving similar nonlinear optimization problems.