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Optimization of a Single Flash Geothermal Power Plant Powered by a Trans-Critical Carbon Dioxide Cycle Using Genetic Algorithm and Nelder-Mead Simplex Method 认领 引用
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作者 Yashar Aryanfar Jorge Luis García Alcaraz 《Energy Engineering》 EI 2023年第2期263-275,共13页
The usage of renewable energies,including geothermal energy,is expanding rapidly worldwide.The low efficiency of geothermal cycles has consistently highlighted the importance of recovering heat loss for these cycles.T... The usage of renewable energies,including geothermal energy,is expanding rapidly worldwide.The low efficiency of geothermal cycles has consistently highlighted the importance of recovering heat loss for these cycles.This paper proposes a combined power generation cycle(single flash geothermal cycle with trans-critical CO2 cycle)and simulates in the EES(Engineering Equation Solver)software.The results show that the design parameters of the proposed system are significantly improved compared to the BASIC single flash cycle.Then,the proposed approach is optimized using the genetic algorithm and the Nelder-Mead Simplex method.Separator pressure,steam turbine output pressure,and CO2 turbine inlet pressure are three assumed variable parameters,and exergy efficiency is the target parameter.In the default operating mode,the system exergy efficiency was 32%,increasing to 39%using the genetic algorithm and 37%using the Nelder-Mead method. 展开更多
关键词 Optimization geothermal genetic algorithm Nelder-Mead simplex exergy efficiency
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Genetic Nelder-Mead neural network algorithm for fault parameter inversion using GPS data 认领 引用 被引量：2
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作者 Leyang Wang Ranran Xu Fengbin Yu 《Geodesy and Geodynamics》 EI CSCD 2022年第4期386-398,共13页
The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-line... The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-linear algorithms in order to improve the inversion precision of GA.This paper proposes a genetic Nelder-Mead neural network algorithm(GNMNNA).This algorithm uses a neural network algorithm(NNA)to optimize the global search ability of GA.At the same time,the simplex algorithm is used to optimize the local search capability of the GA.Through numerical examples,the stability of the inversion algorithm under different strategies is explored.The experimental results show that the proposed GNMNNA has stronger inversion stability and higher precision compared with the existing algorithms.The effectiveness of GNMNNA is verified by the BodrumeKos earthquake and Monte Cristo Range earthquake.The experimental results show that GNMNNA is superior to GA and NNA in both inversion precision and computational stability.Therefore,GNMNNA has greater application potential in complex earthquake environment. 展开更多
关键词 Fault parameter inversion Genetic algorithm Nelder-Mead simplex algorithm Neural network algorithm
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Learner Phase of Partial Reinforcement Optimizer with Nelder-Mead Simplex for Parameter Extraction of Photovoltaic Models 认领 引用
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作者 Jinpeng Huang Zhennao Cai +3 位作者 Ali Asghar Heidari Lei Liu Huiling Chen Guoxi Liang 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第6期3041-3075,共35页
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
关键词 Partial reinforcement optimizer Learner phase Nelder-Mead simplex algorithm Parameter extraction
Localization of Acoustic Emission Source in Rock Using SMIGWO Algorithm 认领 引用 被引量：2
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作者 Jiong Wei Fuqiang Gao +2 位作者 Jinfu Lou Lei Yang Xiaoqing Wang 《International Journal of Coal Science & Technology》 SCIE EI CAS CSCD 2025年第2期42-51,共10页
The Grey Wolf Optimization(GWO)algorithm is acknowledged as an effective method for rock acoustic emission localization.However,the conventional GWO algorithm encounters challenges related to solution accuracy and con... The Grey Wolf Optimization(GWO)algorithm is acknowledged as an effective method for rock acoustic emission localization.However,the conventional GWO algorithm encounters challenges related to solution accuracy and convergence speed.To address these concerns,this paper develops a Simplex Improved Grey Wolf Optimizer(SMIGWO)algorithm.The randomly generating initial populations are replaced with the iterative chaotic sequences.The search process is optimized using the convergence factor optimization algorithm based on the inverse incompleteГfunction.The simplex method is utilized to address issues related to poorly positioned grey wolves.Experimental results demonstrate that,compared to the conventional GWO algorithm-based AE localization algorithm,the proposed algorithm achieves a higher solution accuracy and showcases a shorter search time.Additionally,the algorithm demonstrates fewer convergence steps,indicating superior convergence efficiency.These findings highlight that the proposed SMIGWO algorithm offers enhanced solution accuracy,stability,and optimization performance.The benefits of the SMIGWO algorithm extend universally across various materials,such as aluminum,granite,and sandstone,showcasing consistent effectiveness irrespective of material type.Consequently,this algorithm emerges as a highly effective tool for identifying acoustic emission signals and improving the precision of rock acoustic emission localization. 展开更多
关键词 Acoustic emission Source localization Iterative chaotic mapping Simplex method Grey wolf optimizer algorithm
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Fresh views on some recent developments in the simplex algorithm 认领 引用
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作者 胡剑峰 潘平奇 《Journal of Southeast University(English Edition)》 EI CAS 2008年第1期124-126,共3页
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
关键词 linear programming simplex algorithm pivot mostobtuse-angle nested pricing large-scale problem
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Easy Simplex (AHA Simplex) Algorithm 认领 引用 被引量：1
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作者 A. H. Ansari 《Journal of Applied Mathematics and Physics》 2019年第1期23-30,共8页
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
关键词 Linear Programming Simplex Algorithm Optimal Solution Easy Simplex Algorithm AHA Simplex Algorithm
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一种基于改进Simplex噪声的虚拟地形生成方法 认领 引用
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作者 沈博 张健钦 +1 位作者 马帅豹 文政 《系统仿真学报》 EI CAS CSCD 北大核心 2025年第10期2605-2612,共8页
针对传统虚拟地形生成方法存在的计算复杂度高、生成速度慢、真实感不足等问题,提出了一种基于改进Simplex噪声的虚拟地形生成方法。利用Simplex噪声计算效率高、硬件开销低、随机性更自然等优势,构建基础地形模板;引入分形算法,通过多... 针对传统虚拟地形生成方法存在的计算复杂度高、生成速度慢、真实感不足等问题,提出了一种基于改进Simplex噪声的虚拟地形生成方法。利用Simplex噪声计算效率高、硬件开销低、随机性更自然等优势,构建基础地形模板;引入分形算法,通过多频率、多振幅的噪声叠加增强地形细节层次;结合湍流算法,增加随机扰动和复杂性,从而进一步提高地形的自然感和多样性。仿真结果表明:该方法在地形生成效率、真实感,以及多样性方面均优于传统生成方法。 展开更多
关键词 虚拟地形 Simplex噪声 分形算法 湍流算法 改进Simplex
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A Primal-Dual Simplex Algorithm for Solving Linear Programming Problems with Symmetric Trapezoidal Fuzzy Numbers 认领 引用 被引量：2
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作者 Ali Ebrahimnejad 《Applied Mathematics》 2011年第6期676-684,共9页
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
关键词 Fuzzy Linear Programming Fuzzy Arithmetic Fuzzy Orders Primal-Dual Simplex Algorithm
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New Optimal Pivot Rule for the Simplex Algorithm 认领 引用
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作者 Jean Bosco Etoa Etoa 《Advances in Pure Mathematics》 2016年第10期647-658,共12页
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
关键词 Linear Programming Simplex Algorithm Pivot Rules Optimal Pivot Rule
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A Computational Comparison of Basis Updating Schemes for the Simplex Algorithm on a CPU-GPU System 认领 引用
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作者 Nikolaos Ploskas Nikolaos Samaras 《American Journal of Operations Research》 2013年第6期497-505,共9页
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
关键词 Simplex Algorithm Basis Inverse Graphics Processing Unit MATLAB Compute Unified Device Architecture
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Hybrid Improved Self-adaptive Differential Evolution and Nelder-Mead Simplex Method for Solving Constrained Real-Parameters 认领 引用
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作者 Ngoc-Tam Bui Hieu Pham Hiroshi Hasegawa 《Journal of Mechanics Engineering and Automation》 2013年第9期551-559,共9页
In this paper, a new hybrid algorithm based on exploration power of a new improvement self-adaptive strategy for controlling parameters in DE （differential evolution） algorithm and exploitation capability of Nelder-... In this paper, a new hybrid algorithm based on exploration power of a new improvement self-adaptive strategy for controlling parameters in DE （differential evolution） algorithm and exploitation capability of Nelder-Mead simplex method is presented （HISADE-NMS）. The DE has been used in many practical cases and has demonstrated good convergence properties. It has only a few control parameters as number of particles （NP）, scaling factor （F） and crossover control （CR）, which are kept fixed throughout the entire evolutionary process. However, these control parameters are very sensitive to the setting of the control parameters based on their experiments. The value of control parameters depends on the characteristics of each objective function, therefore, we have to tune their value in each problem that mean it will take too long time to perform. In the new manner, we present a new version of the DE algorithm for obtaining self-adaptive control parameter settings. Some modifications are imposed on DE to improve its capability and efficiency while being hybridized with Nelder-Mead simplex method. To valid the robustness of new hybrid algorithm, we apply it to solve some examples of structural optimization constraints. 展开更多
关键词 Differential evolution hybrid algorithms evolutionary computation global search local search simplex method.
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基于NEWUOA的CRS叠加成像技术 认领 引用
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作者 韩复兴 王源 +4 位作者 高正辉 常志邈 马飞 秦昊 尚浩 《吉林大学学报（地球科学版）》 CAS CSCD 北大核心 2026年第1期386-396,共11页
共反射面元(common reflection surface,CRS)叠加成像技术通过使用相邻共中心点道集信息提高地震数据信噪比,是处理低信噪比、低覆盖次数地震数据的关键技术。其三参数优化的搜索方法无论是经典的分步搜索还是同步的全局搜索,最终都需... 共反射面元(common reflection surface,CRS)叠加成像技术通过使用相邻共中心点道集信息提高地震数据信噪比,是处理低信噪比、低覆盖次数地震数据的关键技术。其三参数优化的搜索方法无论是经典的分步搜索还是同步的全局搜索,最终都需要局部优化算法提升精度,而传统的Nelder-Mead单纯形算法搜索方式简单,计算效率较低。本文采用一种无导数优化算法——新型无约束优化算法(new unconstrained optimization algorithm,NEWUOA)作为CRS叠加的精度提升算法。该算法通过动态构建二次插值模型,采用信赖域迭代策略,利用目标函数曲率信息提升计算效率。文中采用层状起伏模型和Marmousi2模型对该算法进行测试。结果显示,针对简单的层状起伏模型和复杂的Marmousi2模型,基于NEWUOA得到的相干值优化结果均普遍优于或等于基于Nelder-Mead单纯形算法得到的相干值优化结果,与Nelder-Mead单纯形算法相比,计算效率分别提升约48%和45%。这表明NEWUOA在保证CRS叠加优化精度的同时,能够显著提升计算效率,适合作为精度提升的计算工具。 展开更多
关键词 CRS叠加成像技术 三参数优化 NEWUOA 无导数优化 Nelder-Mead单纯形算法
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基于Nelder-Mead单纯形算法的设计反应谱标定方法 认领 引用 被引量：12
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作者 牛洁 薄景山 +2 位作者 王福昌 李琪 吴良杰 《地震工程与工程振动》 CSCD 北大核心 2021年第5期165-175,共11页
规准谱形状的规定和参数的优化是设计反应谱标定的核心问题。Nelder-Mead单纯形算法是求解多参数优化问题的搜索算法,可解决多变量函数的最小化问题。该算法有极强的局部搜索能力,且不要求目标函数的可导性,因此被广泛地应用于工程领域... 规准谱形状的规定和参数的优化是设计反应谱标定的核心问题。Nelder-Mead单纯形算法是求解多参数优化问题的搜索算法,可解决多变量函数的最小化问题。该算法有极强的局部搜索能力,且不要求目标函数的可导性,因此被广泛地应用于工程领域。文中通过提取第一拐点周期、特征周期、平台值和衰减指数4个参数作为标定参数,利用MATLAB软件基于Nelder-Mead单纯形算法对设计反应谱进行标定,并论证了该算法用于设计反应谱标定的合理性和适宜性。以El Centro地震动结论和汶川地震的地震动记录的标定为例,对比了Newmark三参数法、双参数法、差分进化算法和Nelder-Mead单纯形算法的标定结果及误差。通过对比和分析发现,Nelder-Mead单纯形算法标定的反应谱计算简单、收敛速度快且更贴近实际的反应谱,误差更小。 展开更多
关键词 设计反应谱 标定方法 Nelder-Mead单纯形算法 标定参数 算法对比
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A ROBUST PHASE-ONLY DIRECT DATA DOMAIN ALGORITHM BASED ON GENERALIZED RAYLEIGH QUOTIENT OPTIMIZATION USING HYBRID GENETIC ALGORITHM 认领 引用 被引量：2
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作者 Shao Wei Qian Zuping Yuan Feng 《Journal of Electronics(China)》 2007年第4期560-566,共7页
A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on gen- eralized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency ... A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on gen- eralized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency and computational speed are improved via the hybrid GA com- posed of standard GA and Nelder-Mead simplex algorithms. First, the objective function, with a form of generalized Rayleigh quotient, is derived via the standard D3LS algorithm. It is then taken as a fitness function and the unknown phases of all adaptive weights are taken as decision variables. Then, the nonlinear optimization is performed via the hybrid GA to obtain the optimized solution of phase-only adaptive weights. As a phase-only adaptive algorithm, the proposed algorithm is sim- pler than conventional algorithms when it comes to hardware implementation. Moreover, it proc- esses only a single snapshot data as opposed to forming sample covariance matrix and operating matrix inversion. Simulation results show that the proposed algorithm has a good signal recovery and interferences nulling performance, which are superior to that of the phase-only D3LS algorithm based on standard GA. 展开更多
关键词 Generalized Rayleigh quotient Hybrid genetic algorithm Phase-only optimization Direct Data Domain Least Squares （D^3LS） algorithm Nelder-Mead simplex algorithm
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A Hybrid Improved Genetic Algorithm and Its Application in Dynamic Optimization Problems of Chemical Processes 认领 引用 被引量：5
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作者 SUN Fan DU Wenli +2 位作者 QI Rongbin QIAN Feng ZHONG Weimin 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2013年第2期144-154,共11页
The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature.13enetic algorithm(GA)has been proved to be a teasibte method when the gradient is di... The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature.13enetic algorithm(GA)has been proved to be a teasibte method when the gradient is difficult to calculate.Its advantage is that the control profiles at all time stages are optimized simultaneously,but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum.In this study,a hybrid improved genetic algorithm(HIGA)for solving dynamic optimization problems is proposed to overcome these defects.Simplex method(SM)is used to perform the local search in the neighborhood of the optimal solution.By using SM,the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved.The hybrid algorithm presents some improvements,such as protecting the best individual,accepting immigrations,as well as employing adaptive crossover and Ganssian mutation operators.The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems.At last,HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable. 展开更多
关键词 genetic algorithm simplex method dynamic optimization chemical process
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A novel particle swarm optimizer without velocity:Simplex-PSO 认领 引用 被引量：8
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作者 肖宏峰 谭冠政 《Journal of Central South University》 SCIE EI CAS 2010年第2期349-356,共8页
A simplex particle swarm optimization(simplex-PSO) derived from the Nelder-Mead simplex method was proposed to optimize the high dimensionality functions.In simplex-PSO,the velocity term was abandoned and its referenc... A simplex particle swarm optimization(simplex-PSO) derived from the Nelder-Mead simplex method was proposed to optimize the high dimensionality functions.In simplex-PSO,the velocity term was abandoned and its reference objectives were the best particle and the centroid of all particles except the best particle.The convergence theorems of linear time-varying discrete system proved that simplex-PSO is of consistent asymptotic convergence.In order to reduce the probability of trapping into a local optimal value,an extremum mutation was introduced into simplex-PSO and simplex-PSO-t(simplex-PSO with turbulence) was devised.Several experiments were carried out to verify the validity of simplex-PSO and simplex-PSO-t,and the experimental results confirmed the conclusions:(1) simplex-PSO-t can optimize high-dimension functions with 200-dimensionality;(2) compared PSO with chaos PSO(CPSO),the best optimum index increases by a factor of 1×102-1×104. 展开更多
关键词 Nelder-Mead simplex method particle swarm optimizer high-dimension function optimization convergence analysis
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基于Simplex算法的高压直流输电分段变速率VDCOL研究 认领 引用 被引量：9
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作者 冯明 李兴源 +2 位作者 李妮 王超 洪潮 《四川大学学报（工程科学版）》 CSCD 北大核心 2015年第4期162-167,共6页
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
关键词 高压直流输电 低压限流单元 电压稳定 Simplex算法
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基于Simplex算法的VSC-HVDC控制参数优化 认领 引用 被引量：15
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作者 郭春义 赵成勇 +2 位作者 李广凯 饶宏 黎小林 《电力自动化设备》 EI 北大核心 2010年第9期13-17,共5页
以电压源换流器高压直流输电(VSC-HVDC)的稳态模型为基础,根据VSC功率传输方程的直角坐标形式,采用逆系统方法和PI控制相结合的方法设计了VSC的有功功率和无功功率独立调节的VSC-HVDC控制系统。基于非线性单纯形Simplex算法,对PI控制参... 以电压源换流器高压直流输电(VSC-HVDC)的稳态模型为基础,根据VSC功率传输方程的直角坐标形式,采用逆系统方法和PI控制相结合的方法设计了VSC的有功功率和无功功率独立调节的VSC-HVDC控制系统。基于非线性单纯形Simplex算法,对PI控制参数进行优化。PSCAD/EMTDC下的仿真结果表明,通过参数优化,VSC-HVDC控制目标的最大误差百分比低于1.5%;同时也证明所设计的控制系统能独立控制有功功率和无功功率。 展开更多
关键词 电压源换流器 高压直流输电 控制系统 Simplex算法 PI参数优化
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Bald Eagle Search Optimization Algorithm Combined with Spherical Random Shrinkage Mechanism and Its Application 认领 引用 被引量：1
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作者 Wenyan Guo Zhuolin Hou +2 位作者 Fang Dai Xiaoxia Wang Yufan Qiang 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第1期572-605,共34页
Over the last two decades,stochastic optimization algorithms have proved to be a very promising approach to solving a variety of complex optimization problems.Bald eagle search optimization(BES)as a new stochastic opt... Over the last two decades,stochastic optimization algorithms have proved to be a very promising approach to solving a variety of complex optimization problems.Bald eagle search optimization(BES)as a new stochastic optimization algorithm with fast convergence speed has the ability of prominent optimization and the defect of collapsing in the local best.To avoid BES collapse at local optima,inspired by the fact that the volume of the sphere is the largest when the surface area is certain,an improved bald eagle search optimization algorithm(INMBES)integrating the random shrinkage mechanism of the sphere is proposed.Firstly,the INMBES embeds spherical coordinates to design a more accurate parameter update method to modify the coverage and dispersion of the population.Secondly,the population splits into elite and non-elite groups and the Bernoulli chaos is applied to elite group to tap around potential solutions of the INMBES.The non-elite group is redistributed again and the Nelder-Mead simplex strategy is applied to each group to accelerate the evolution of the worst individual and the convergence process of the INMBES.The results of Friedman and Wilcoxon rank sum tests of CEC2017 in 10,30,50,and 100 dimensions numerical optimization confirm that the INMBES has superior performance in convergence accuracy and avoiding falling into local optimization compared with other potential improved algorithms but inferior to the champion algorithm and ranking third.The three engineering constraint optimization problems and 26 real world problems and the problem of extracting the best feature subset by encapsulated feature selection method verify that the INMBES’s performance ranks first and has achieved satisfactory accuracy in solving practical problems. 展开更多
关键词 Bald eagle search optimization algorithm Spherical coordinates Chaotic variation Simplex method Encapsulated feature selection
基于Downhill-Simplex算法的观测数据与作物生长模型同化方法研究 认领 引用 被引量：6
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作者 孙琳丽 景元书 +4 位作者 马玉平 俄有浩 邹艳东 邢开瑜 吴玮 《中国农业气象》 CSCD 北大核心 2012年第4期555-566,共12页
以夏玉米叶面积指数(LAI)、贮存器官干重(WSO)、地上总干重(TAGP)以及土壤水分含量(SM)为结合点,建立了基于Downhill-Simplex算法的作物生长模型WOFOST同化多种地面观测数据的一般方法或流程:开展观测数据与作物生长模型同化方法的正确... 以夏玉米叶面积指数(LAI)、贮存器官干重(WSO)、地上总干重(TAGP)以及土壤水分含量(SM)为结合点,建立了基于Downhill-Simplex算法的作物生长模型WOFOST同化多种地面观测数据的一般方法或流程:开展观测数据与作物生长模型同化方法的正确性验证→利用Downhill-Simplex算法进行WOFOST模型的敏感性分析→选择敏感参数组合→通过优化效果确定待优化参数→利用新的观测数据对待优化参数进行优化,从而实现了观测数据与作物生长模型的同化,提升了模型的模拟能力。同化过程中遴选出的WOFOST模型的待优化参数主要包括比叶面积、最大CO2同化速率、初始地上部总干物重、根深最大日增量和初始土壤有效水等。 展开更多
关键词 观测数据同化 作物生长模型 Downhill—Simplex算法 敏感性分析
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