First, the main procedures and the distinctive features of the most-obtuse-angle（MOA）row or column pivot rules are introduced for achieving primal or dual feasibility in linear programming. Then, two special auxilia...First, the main procedures and the distinctive features of the most-obtuse-angle（MOA）row or column pivot rules are introduced for achieving primal or dual feasibility in linear programming. Then, two special auxiliary problems are constructed to prove that each of the rules can be actually considered as a simplex approach for solving the corresponding auxiliary problem. In addition, the nested pricing rule is also reviewed and its geometric interpretation is offered based on the heuristic characterization of an optimal solution.展开更多
Evolutionary computation based on the idea of biologic evolution is one type of global optimization algorithm that uses self-adaptation,self-organization and random searching to solve optimization problems.The evoluti...Evolutionary computation based on the idea of biologic evolution is one type of global optimization algorithm that uses self-adaptation,self-organization and random searching to solve optimization problems.The evolutionary-simplex algorithm is introduced in this paper.It contains floating encoding which combines the evolutionary computation and the simplex algorithm to overcome the problems encountered in the genetic algorithm and evolutionary strategy methods. Numerical experiments are performed using seven typical functions to verify the algorithm.An inverse analysis method to identify structural physical parameters based on incomplete dynamic responses obtained from the analysis in the time domain is presented by using the evolutionary-simplex algorithm.The modal evolutionary-simplex algorithm converted from the time domain to the modal domain is proposed to improve the inverse efficiency.Numerical calculations for a 50-DOF system show that when compared with other methods,the evolutionary-simplex algorithm offers advantages of high precision, efficient searching ability,strong ability to resist noise,independence of initial value,and good adaptation to incomplete information conditions.展开更多
In this paper, a hybrid simplex-improved genetic algorithm (HSIGA) which combines simplex method (SM) and genetic algorithm (GA) is proposed to solve global numerical optimization problems. In this hybrid algorithm so...In this paper, a hybrid simplex-improved genetic algorithm (HSIGA) which combines simplex method (SM) and genetic algorithm (GA) is proposed to solve global numerical optimization problems. In this hybrid algorithm some improved genetic mechanisms, for example, non-linear ranking selection, competition and selection among several crossover offspring, adaptive change of mutation scaling and stage evolution, are adopted; and new population is produced through three ap-proaches, i.e. elitist strategy, modified simplex strategy and improved genetic algorithm (IGA) strategy. Numerical experi-ments are included to demonstrate effectiveness of the proposed algorithm.展开更多
Two existing methods for solving a class of fuzzy linear programming (FLP) problems involving symmetric trapezoidal fuzzy numbers without converting them to crisp linear programming problems are the fuzzy primal simpl...Two existing methods for solving a class of fuzzy linear programming (FLP) problems involving symmetric trapezoidal fuzzy numbers without converting them to crisp linear programming problems are the fuzzy primal simplex method proposed by Ganesan and Veeramani [1] and the fuzzy dual simplex method proposed by Ebrahimnejad and Nasseri [2]. The former method is not applicable when a primal basic feasible solution is not easily at hand and the later method needs to an initial dual basic feasible solution. In this paper, we develop a novel approach namely the primal-dual simplex algorithm to overcome mentioned shortcomings. A numerical example is given to illustrate the proposed approach.展开更多
The purpose of this research paper is to introduce Easy Simplex Algorithm which is developed by author. The simplex algorithm first presented by G. B. Dantzing, is generally used for solving a Linear programming probl...The purpose of this research paper is to introduce Easy Simplex Algorithm which is developed by author. The simplex algorithm first presented by G. B. Dantzing, is generally used for solving a Linear programming problem (LPP). One of the important steps of the simplex algorithm is to convert all unequal constraints into equal form by adding slack variables then proceeds to basic solution. Our new algorithm i) solves the LPP without equalize the constraints and ii) leads to optimal solution definitely in lesser time. The goal of suggested algorithm is to improve the simplex algorithm so that the time of solving an LPP will be definitely lesser than the simplex algorithm. According to this Easy Simplex (AHA Simplex) Algorithm the use of Big M method is not required.展开更多
The purpose of this paper is to introduce a new pivot rule of the simplex algorithm. The simplex algorithm first presented by George B. Dantzig, is a widely used method for solving a linear programming problem (LP). O...The purpose of this paper is to introduce a new pivot rule of the simplex algorithm. The simplex algorithm first presented by George B. Dantzig, is a widely used method for solving a linear programming problem (LP). One of the important steps of the simplex algorithm is applying an appropriate pivot rule to select the basis-entering variable corresponding to the maximum reduced cost. Unfortunately, this pivot rule not only can lead to a critical cycling (solved by Bland’s rules), but does not improve efficiently the objective function. Our new pivot rule 1) solves the cycling problem in the original Dantzig’s simplex pivot rule, and 2) leads to an optimal improvement of the objective function at each iteration. The new pivot rule can lead to the optimal solution of LP with a lower number of iterations. In a maximization problem, Dantzig’s pivot rule selects a basis-entering variable corresponding to the most positive reduced cost;in some problems, it is well-known that Dantzig’s pivot rule, before reaching the optimal solution, may visit a large number of extreme points. Our goal is to improve the simplex algorithm so that the number of extreme points to visit is reduced;we propose an optimal improvement in the objective value per unit step of the basis-entering variable. In this paper, we propose a pivot rule that can reduce the number of such iterations over the Dantzig’s pivot rule and prevent cycling in the simplex algorithm. The idea is to have the maximum improvement in the objective value function: from the set of basis-entering variables with positive reduced cost, the efficient basis-entering variable corresponds to an optimal improvement of the objective function. Using computational complexity arguments and some examples, we prove that our optimal pivot rule is very effective and solves the cycling problem in LP. We test and compare the efficiency of this new pivot rule with Dantzig’s original pivot rule and the simplex algorithm in MATLAB environment.展开更多
The computation of the basis inverse is the most time-consuming step in simplex type algorithms. This inverse does not have to be computed from scratch at any iteration, but updating schemes can be applied to accelera...The computation of the basis inverse is the most time-consuming step in simplex type algorithms. This inverse does not have to be computed from scratch at any iteration, but updating schemes can be applied to accelerate this calculation. In this paper, we perform a computational comparison in which the basis inverse is computed with five different updating schemes. Then, we propose a parallel implementation of two updating schemes on a CPU-GPU System using MATLAB and CUDA environment. Finally, a computational study on randomly generated full dense linear programs is preented to establish the practical value of GPU-based implementation.展开更多
This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed LNPRO.The LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,c...This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed LNPRO.The LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,changing the state of the PRO in terms of self-strengthening.Furthermore,the Nelder-Mead simplex is used to optimize the best agent in the population,accelerating the convergence speed and improving the accuracy of the PRO population.By comparing LNPRO with nine advanced algorithms in the IEEE CEC 2022 benchmark function,the convergence accuracy of the LNPRO has been verified.The accuracy and stability of simulated data and real data in the parameter extraction of PV systems are crucial.Compared to the PRO,the precision and stability of LNPRO have indeed been enhanced in four types of photovoltaic components,and it is also superior to other excellent algorithms.To further verify the parameter extraction problem of LNPRO in complex environments,LNPRO has been applied to three types of manufacturer data,demonstrating excellent results under varying irradiation and temperatures.In summary,LNPRO holds immense potential in solving the parameter extraction problems in PV systems.展开更多
为了改善高压直流系统的故障后的恢复性能,在研究低压限流单元(voltage dependent current order limiter,VDCOL)对直流系统无功功率消耗和电压稳定性影响的基础上,提出了一种分段变速率低压限流单元(piecewise-variable-rate VDCOL,PVR...为了改善高压直流系统的故障后的恢复性能,在研究低压限流单元(voltage dependent current order limiter,VDCOL)对直流系统无功功率消耗和电压稳定性影响的基础上,提出了一种分段变速率低压限流单元(piecewise-variable-rate VDCOL,PVR-VDCOL)的控制方法,该方法通过将电压下降或恢复过程划分为几个不同的阶段,并在每个阶段根据电压水平的不同而设置不同的功率恢复速率。推导了控制器初值的计算公式,制定了利用Simplex算法优化控制器参数的流程,并重点分析了分段数目对控制器性能的影响及其确定方法。在PSCAD/EMTDC中对提出的PVR-VDCOL和传统线性VDCOL的控制效果进行了对比仿真,并对不同分段数目下的仿真结果进行了对比分析,仿真结果表明提出的PVR-VDCOL能够有效改善直流系统的恢复性能。展开更多
料筒温度是影响注塑成型产品质量的关键参数之一,比例积分微分(proportional integral differential,PID)控制器在料筒温度控制系统中广泛应用。然而,PID控制器参数优化依赖操作者经验,存在成本高、效率低、精度差的问题。为解决上述问...料筒温度是影响注塑成型产品质量的关键参数之一,比例积分微分(proportional integral differential,PID)控制器在料筒温度控制系统中广泛应用。然而,PID控制器参数优化依赖操作者经验,存在成本高、效率低、精度差的问题。为解决上述问题,围绕料筒温度PID控制器参数优化展开研究。首先,针对料筒温度控制系统性能优化,提出性能评价指标,设计PID控制器参数优化框架;然后,基于知识指引型数据驱动优化策略的思想,对单纯形搜索算法进行改进,提出了基于历史梯度近似的知识指引型单纯形搜索算法,以提高PID控制器参数优化效率。实验结果表明,与单纯形搜索算法相比,改进后的算法以牺牲少量优化精度为代价,显著提升了优化效率,降低了PID控制器参数优化成本。展开更多
基金The National Natural Science Foundation of China(No.10371017).
文摘First, the main procedures and the distinctive features of the most-obtuse-angle（MOA）row or column pivot rules are introduced for achieving primal or dual feasibility in linear programming. Then, two special auxiliary problems are constructed to prove that each of the rules can be actually considered as a simplex approach for solving the corresponding auxiliary problem. In addition, the nested pricing rule is also reviewed and its geometric interpretation is offered based on the heuristic characterization of an optimal solution.
基金National Natural Science Foundation of China(Grant No.50278006)
文摘Evolutionary computation based on the idea of biologic evolution is one type of global optimization algorithm that uses self-adaptation,self-organization and random searching to solve optimization problems.The evolutionary-simplex algorithm is introduced in this paper.It contains floating encoding which combines the evolutionary computation and the simplex algorithm to overcome the problems encountered in the genetic algorithm and evolutionary strategy methods. Numerical experiments are performed using seven typical functions to verify the algorithm.An inverse analysis method to identify structural physical parameters based on incomplete dynamic responses obtained from the analysis in the time domain is presented by using the evolutionary-simplex algorithm.The modal evolutionary-simplex algorithm converted from the time domain to the modal domain is proposed to improve the inverse efficiency.Numerical calculations for a 50-DOF system show that when compared with other methods,the evolutionary-simplex algorithm offers advantages of high precision, efficient searching ability,strong ability to resist noise,independence of initial value,and good adaptation to incomplete information conditions.
基金Supported by National Natural Science Foundation of P.R.China(60474069)
文摘In this paper, a hybrid simplex-improved genetic algorithm (HSIGA) which combines simplex method (SM) and genetic algorithm (GA) is proposed to solve global numerical optimization problems. In this hybrid algorithm some improved genetic mechanisms, for example, non-linear ranking selection, competition and selection among several crossover offspring, adaptive change of mutation scaling and stage evolution, are adopted; and new population is produced through three ap-proaches, i.e. elitist strategy, modified simplex strategy and improved genetic algorithm (IGA) strategy. Numerical experi-ments are included to demonstrate effectiveness of the proposed algorithm.
文摘Two existing methods for solving a class of fuzzy linear programming (FLP) problems involving symmetric trapezoidal fuzzy numbers without converting them to crisp linear programming problems are the fuzzy primal simplex method proposed by Ganesan and Veeramani [1] and the fuzzy dual simplex method proposed by Ebrahimnejad and Nasseri [2]. The former method is not applicable when a primal basic feasible solution is not easily at hand and the later method needs to an initial dual basic feasible solution. In this paper, we develop a novel approach namely the primal-dual simplex algorithm to overcome mentioned shortcomings. A numerical example is given to illustrate the proposed approach.
文摘The purpose of this research paper is to introduce Easy Simplex Algorithm which is developed by author. The simplex algorithm first presented by G. B. Dantzing, is generally used for solving a Linear programming problem (LPP). One of the important steps of the simplex algorithm is to convert all unequal constraints into equal form by adding slack variables then proceeds to basic solution. Our new algorithm i) solves the LPP without equalize the constraints and ii) leads to optimal solution definitely in lesser time. The goal of suggested algorithm is to improve the simplex algorithm so that the time of solving an LPP will be definitely lesser than the simplex algorithm. According to this Easy Simplex (AHA Simplex) Algorithm the use of Big M method is not required.
文摘The purpose of this paper is to introduce a new pivot rule of the simplex algorithm. The simplex algorithm first presented by George B. Dantzig, is a widely used method for solving a linear programming problem (LP). One of the important steps of the simplex algorithm is applying an appropriate pivot rule to select the basis-entering variable corresponding to the maximum reduced cost. Unfortunately, this pivot rule not only can lead to a critical cycling (solved by Bland’s rules), but does not improve efficiently the objective function. Our new pivot rule 1) solves the cycling problem in the original Dantzig’s simplex pivot rule, and 2) leads to an optimal improvement of the objective function at each iteration. The new pivot rule can lead to the optimal solution of LP with a lower number of iterations. In a maximization problem, Dantzig’s pivot rule selects a basis-entering variable corresponding to the most positive reduced cost;in some problems, it is well-known that Dantzig’s pivot rule, before reaching the optimal solution, may visit a large number of extreme points. Our goal is to improve the simplex algorithm so that the number of extreme points to visit is reduced;we propose an optimal improvement in the objective value per unit step of the basis-entering variable. In this paper, we propose a pivot rule that can reduce the number of such iterations over the Dantzig’s pivot rule and prevent cycling in the simplex algorithm. The idea is to have the maximum improvement in the objective value function: from the set of basis-entering variables with positive reduced cost, the efficient basis-entering variable corresponds to an optimal improvement of the objective function. Using computational complexity arguments and some examples, we prove that our optimal pivot rule is very effective and solves the cycling problem in LP. We test and compare the efficiency of this new pivot rule with Dantzig’s original pivot rule and the simplex algorithm in MATLAB environment.
文摘The computation of the basis inverse is the most time-consuming step in simplex type algorithms. This inverse does not have to be computed from scratch at any iteration, but updating schemes can be applied to accelerate this calculation. In this paper, we perform a computational comparison in which the basis inverse is computed with five different updating schemes. Then, we propose a parallel implementation of two updating schemes on a CPU-GPU System using MATLAB and CUDA environment. Finally, a computational study on randomly generated full dense linear programs is preented to establish the practical value of GPU-based implementation.
基金supported in part by the Natural Science Foundation of Zhejiang Province(LTGS23E070001).
文摘This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed LNPRO.The LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,changing the state of the PRO in terms of self-strengthening.Furthermore,the Nelder-Mead simplex is used to optimize the best agent in the population,accelerating the convergence speed and improving the accuracy of the PRO population.By comparing LNPRO with nine advanced algorithms in the IEEE CEC 2022 benchmark function,the convergence accuracy of the LNPRO has been verified.The accuracy and stability of simulated data and real data in the parameter extraction of PV systems are crucial.Compared to the PRO,the precision and stability of LNPRO have indeed been enhanced in four types of photovoltaic components,and it is also superior to other excellent algorithms.To further verify the parameter extraction problem of LNPRO in complex environments,LNPRO has been applied to three types of manufacturer data,demonstrating excellent results under varying irradiation and temperatures.In summary,LNPRO holds immense potential in solving the parameter extraction problems in PV systems.
文摘为了改善高压直流系统的故障后的恢复性能,在研究低压限流单元(voltage dependent current order limiter,VDCOL)对直流系统无功功率消耗和电压稳定性影响的基础上,提出了一种分段变速率低压限流单元(piecewise-variable-rate VDCOL,PVR-VDCOL)的控制方法,该方法通过将电压下降或恢复过程划分为几个不同的阶段,并在每个阶段根据电压水平的不同而设置不同的功率恢复速率。推导了控制器初值的计算公式,制定了利用Simplex算法优化控制器参数的流程,并重点分析了分段数目对控制器性能的影响及其确定方法。在PSCAD/EMTDC中对提出的PVR-VDCOL和传统线性VDCOL的控制效果进行了对比仿真,并对不同分段数目下的仿真结果进行了对比分析,仿真结果表明提出的PVR-VDCOL能够有效改善直流系统的恢复性能。
文摘料筒温度是影响注塑成型产品质量的关键参数之一,比例积分微分(proportional integral differential,PID)控制器在料筒温度控制系统中广泛应用。然而,PID控制器参数优化依赖操作者经验,存在成本高、效率低、精度差的问题。为解决上述问题,围绕料筒温度PID控制器参数优化展开研究。首先,针对料筒温度控制系统性能优化,提出性能评价指标,设计PID控制器参数优化框架;然后,基于知识指引型数据驱动优化策略的思想,对单纯形搜索算法进行改进,提出了基于历史梯度近似的知识指引型单纯形搜索算法,以提高PID控制器参数优化效率。实验结果表明,与单纯形搜索算法相比,改进后的算法以牺牲少量优化精度为代价,显著提升了优化效率,降低了PID控制器参数优化成本。
