本文提出了一种基于变换函数与填充函数的模糊粒子群优化算法(Fuzzy partical swarm optimization based on filled function and transformation function,FPSO-TF).以基于不同隶属度函数的多回路模糊控制系统为基础,进一步结合变换函...本文提出了一种基于变换函数与填充函数的模糊粒子群优化算法(Fuzzy partical swarm optimization based on filled function and transformation function,FPSO-TF).以基于不同隶属度函数的多回路模糊控制系统为基础,进一步结合变换函数与填充函数,使该算法减少了陷入局部最优的可能,又可以跳出局部极小值点至更小的点,快速高效地搜索到全局最优解.最后采用基准函数对此算法进行测试,并与几种不同类型的改进算法进行对比分析,验证了此算法的有效性与优越性.展开更多
自从1990年Ge R.P.教授在文章【A Filled Function Method for Finding a Global Minimizer of a Function of Several Variables[J].Math.Programming,1990,46:191-204】中提出了求全局最优化的填充函数算法以来,此类算法的有效性一直...自从1990年Ge R.P.教授在文章【A Filled Function Method for Finding a Global Minimizer of a Function of Several Variables[J].Math.Programming,1990,46:191-204】中提出了求全局最优化的填充函数算法以来,此类算法的有效性一直受到调整参数的困扰,在上述文章最后他也期待出现无参数的填充函数.作为一种尝试,本文提出了一种新的无参数的填充函数,并在此基础上,构造出一个无参数填充函数算法.数值试验证明该算法是有效的,同时与已有的填充函数算法比较具有计算量小的优势.展开更多
In this paper, two auxiliary functions for global optimization are proposed. These two auxiliary functions possess all characters of tunnelling functions and filled functions under certain general assumptions. Thus, t...In this paper, two auxiliary functions for global optimization are proposed. These two auxiliary functions possess all characters of tunnelling functions and filled functions under certain general assumptions. Thus, they can be considered as the unification of filled function and tunnelling function. Moreover, the process of tunneling or filling for global optimization can be unified as the minimization of such auxiliary functions. Result of numerical experiments shows that such two auxiliary functions are effective.展开更多
In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parame...In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parameter accuracy.This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function.This approach addresses the topic of particle swarmoptimization in parameter identification from two perspectives.Firstly,the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function,making the extreme point of the fitness function more prominent and improving the algorithm’s search ability while reducing the algorithm’s computational burden.Secondly,on the basis of themulti-loop fuzzy control systembased onmultiplemembership functions,it is merged with the filled function to improve the algorithm’s capacity to skip out of the local optimal solution.This approach can be used to identify the parameters of permanent magnet synchronous motors by sampling only the stator current,voltage,and speed data.The simulation results show that the method can effectively identify the electrical parameters of a permanent magnet synchronous motor,and it has superior global convergence performance and robustness.展开更多
文摘本文提出了一种基于变换函数与填充函数的模糊粒子群优化算法(Fuzzy partical swarm optimization based on filled function and transformation function,FPSO-TF).以基于不同隶属度函数的多回路模糊控制系统为基础,进一步结合变换函数与填充函数,使该算法减少了陷入局部最优的可能,又可以跳出局部极小值点至更小的点,快速高效地搜索到全局最优解.最后采用基准函数对此算法进行测试,并与几种不同类型的改进算法进行对比分析,验证了此算法的有效性与优越性.
文摘自从1990年Ge R.P.教授在文章【A Filled Function Method for Finding a Global Minimizer of a Function of Several Variables[J].Math.Programming,1990,46:191-204】中提出了求全局最优化的填充函数算法以来,此类算法的有效性一直受到调整参数的困扰,在上述文章最后他也期待出现无参数的填充函数.作为一种尝试,本文提出了一种新的无参数的填充函数,并在此基础上,构造出一个无参数填充函数算法.数值试验证明该算法是有效的,同时与已有的填充函数算法比较具有计算量小的优势.
基金Supported by the National Natural Science Foundation of China(No.70471012)
文摘In this paper, two auxiliary functions for global optimization are proposed. These two auxiliary functions possess all characters of tunnelling functions and filled functions under certain general assumptions. Thus, they can be considered as the unification of filled function and tunnelling function. Moreover, the process of tunneling or filling for global optimization can be unified as the minimization of such auxiliary functions. Result of numerical experiments shows that such two auxiliary functions are effective.
基金the Natural Science Foundation of China under Grant 52077027in part by the Liaoning Province Science and Technology Major Project No.2020JH1/10100020.
文摘In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parameter accuracy.This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function.This approach addresses the topic of particle swarmoptimization in parameter identification from two perspectives.Firstly,the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function,making the extreme point of the fitness function more prominent and improving the algorithm’s search ability while reducing the algorithm’s computational burden.Secondly,on the basis of themulti-loop fuzzy control systembased onmultiplemembership functions,it is merged with the filled function to improve the algorithm’s capacity to skip out of the local optimal solution.This approach can be used to identify the parameters of permanent magnet synchronous motors by sampling only the stator current,voltage,and speed data.The simulation results show that the method can effectively identify the electrical parameters of a permanent magnet synchronous motor,and it has superior global convergence performance and robustness.
基金the National Natural Science Foundation of China(12071112,11471102)Basic Research Projects for Key Scientific Research Projects in Henan Province of China(20ZX001)。
