自适应灰狼优化算法及纸浆浓度控制应用

Application of Adaptive Grey Wolf Optimization Algorithm and Pulp Concentration Control

中文关键词: 灰狼算法  聚焦距离变化率  收敛因子  自适应权重因子  PID参数  纸浆浓度

英文关键词: grey wolf optimization  focusing distance changing rate  convergence factor  adaptive weighting factor  PID parameters  pulp concentration

中文摘要:

      目的 针对基本灰狼算法在函数优化过程中精度低、收敛速度慢、局部搜索能力差等问题，提出一种基于收敛因子和权重动态变化的自适应灰狼优化算法。方法 为了平衡算法的全局和局部搜索能力，引入聚焦距离变化率来动态调整收敛因子;使用自适应权重因子来改变算法的位置更新公式，以提高算法的收敛速度和精度。结果 仿真实验结果表明，改进后的算法在收敛精度和速度上都有了显著的提升，并且克服了灰狼算法在处理多峰函数时易陷入局部最优的缺点;对于纸浆浓度控制系统，控制效果更加理想。结论 通过改进的灰狼算法对PID控制器参数进行整定，可以显著提高系统的控制精度和其他性能指标，能更好地满足实际应用的要求。

英文摘要:

      The work aims to propose an adaptive gray wolf optimization algorithm based on the convergence factor and dynamic changes of weights to solve problems such as low precision, slow convergence rate and poor local search ability of basic wolf algorithm in function optimization. A focusing distance changing rate for dynamically updating the convergence factor was given to maintain a balance between global search and local search of the algorithm. The position updating formula of the algorithm was adjusted by introducing the adaptive weighting factor, to improve the convergence speed and precision of the algorithm. The simulation results showed that the improved algorithm had a significant improvement in convergence accuracy and speed, and overcame the shortcoming of the gray wolf algorithm that it was easy to fall into a local optimum when processing multi-modal functions. For pulp concentration control systems, the control effect was relatively ideal. The PID controller parameters set by the improved gray wolf algorithm can obviously improve the performance indicators such as the control accuracy of the system, and can better meet the requirements of practical application.