检索结果-内蒙古大学图书馆

A dynamic Gaussian process surrogate model based on the grasshopper optimization algorithm for non-probabilistic reliability analysis of complex structures

引用

STRUCTURE AND INFRASTRUCTURE ENGINEERING 2025年第4期21卷 616-627页

作者： Wang, Junmu Su, Guoshao Hao, Junmeng Guangxi Univ Sch Civil & Architecture Engn Key Lab Disaster Prevent & Struct Safety Minist Educ Nanning Peoples R China Guangxi Univ Guangxi Prov Engn Res Ctr Water Secur & Intelligen Nanning Peoples R China Guangxi Univ Guangxi Key Lab Disaster Prevent & Engn Safety Nanning Peoples R China Guangxi Univ Sch Civil & Architecture Engn Key Lab Disaster Prevent & Struct Safety Minist Educ Nanning 530004 Peoples R China

In practical complex engineering structures, the performance function (PF) for reliability analysis is commonly implicit and highly nonlinear. Commonly used surrogate models are appropriate for structural reliability analysis with an implicit PF. However, these methods require hundreds of PF values, which are time-consuming to obtain by adopting numerical analysis, such as finite element analysis (FEA). Therefore, since non-probabilistic reliability analysis does not require a large number of samples, it has great development potential. In this paper, a dynamic Gaussian process (GP) surrogate model based on the grasshopper optimization algorithm (DGP-GOA) is proposed for the non-probabilistic reliability analysis. First, with the help of the scale factor of the convex set model, the non-probabilistic reliability analysis problem is transformed into an unconstrained optimization problem. Second, the DGP-GOA fits the PF by constructing a GP surrogate model with a small dataset. Third, the GOA is used to search for the global optimal solution to obtain a non-probabilistic reliability index. Then, a dynamic retraining strategy is proposed to improve the fitting accuracy and efficiency. The results demonstrate that the proposed method is highly applicable to the non-probabilistic structural reliability analysis of complex engineering structures.

关键词： Finite element analysis Gaussian process grasshopper optimization algorithm non-probabilistic probabilistic theory performance function structural reliability surrogate model

来源：评论

学校读者我要写书评

暂无评论

A binary grasshopper optimization algorithm for solving uncapacitated facility location problem

引用

ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2025年 65卷

作者： Babalik, Ahmet Babadag, Aybuke Konya Tech Univ Fac Engn & Nat Sci Dept Comp Engn TR-42075 Konya Turkiye

The Uncapacitated Facility Location Problem (UFLP) is a real-world binary optimization problem that aims to find the number of facilities to open, minimizing the total cost of exchange between customers and facilities, as well as the opening costs of these facilities. UFLP is classified as an NP-Hard problem. Metaheuristic methods are often preferred to solve UFLP due to their ability to find acceptable solutions in a reasonable time and its NPHard characteristics. grasshopper optimization algorithm (GOA) is a continuous metaheursitics optimization algorithm. In the literature, although some binary versions of the GOA algorithm have been used to solve problems such as feature selection, knapsack, scheduling and cluster covering, its performance analysis has not been conducted on UFLP which is pure binary optimization problem. In this study, a novel binary version of the GOA integrated with a novel binarization procedure is proposed for solving the UFLP. In the binarization procedure, a probability-based update strategy has been developed for generating new candidate solutions. This approach ensures the probability of determining the effect of the global best solution on the candidate solution. Besides, during the population update phase, there are two different mechanisms to update the global best solution and other grasshoppers. To enhance diversity in the grasshopper population, an alpha parameter has been integrated into the original algorithm. It was aimed to improve the quality of the candidate solutions by the integration of the alpha parameter. The performance of the proposed algorithm is assessed on the CAP and M* datasets. The obtained GAP values for the CAP 71-CAP A, CAP B, and CAP C problems are 0, 0.14, and 0.18, respectively. The GAP is 0 for the MO1-MO5, MP2, MP3, and MQ1-MQ3 problems, and GAP <= 0.24 for the remaining problems. The results were compared with other state-of-the-art binary optimization algorithms. The experimental results show that the

关键词： Binary optimization Discrete optimization grasshopper optimization algorithm Uncapacitated facility location problem

来源：评论

学校读者我要写书评

暂无评论

grasshopper optimization algorithm utilized Xilinx controller for maximum power generation in photovoltaic system

引用

EVOLVING SYSTEMS 2021年第4期12卷 885-898页

作者： Ram, B. V. Kalyan Chidambararaj, N. Ideal Inst Technol Dept Elect & Commun Engn Kakinada Andhra Pradesh India St Josephs Coll Engn Dept Elect & Elect Engn Chennai Tamil Nadu India

A novel grasshopper optimization (GOA) based Xilinx System Generator (XSG) controller is proposed for the purpose of evaluating the Maximum Power Point Tracking in the grid integrated Photovoltaic (PV) power generation system. The proposed controller functions with the assistance of the GOA algorithm and the XSG procedures. The innovation of the controller is intended to collect the maximum power produced from the PV array in accordance with the solar irradiance and temperature of the array. The proposed GOA algorithm based XSG controller is achieved by maximum power from the PV array, it is necessary to adjust the switching signal for the voltage source inverter (VSI). The proposed PV structure is elegantly designed in the mighty platform of the MATLAB/Simulink and the switching schemes are produced in accordance with the XSG controller. At last, the output response of the presented GOA based XSG controller is analyzed and compared with conventional techniques such as the Particle Swarm optimization (PSO) and the Artificial Bee Colony (ABC) algorithms.

关键词： Photovoltaic array Maximum power point tracking grasshopper optimization algorithm Xilinx system generator controller Voltage source inverter Particle swarm optimization Artificial bee colony

来源：评论

学校读者我要写书评

暂无评论

grasshopper optimization algorithm-based approach for the optimization of ensemble classifier and feature selection to classify epileptic EEG signals

引用

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2019年第6期57卷 1323-1339页

作者： Singh, Gurwinder Singh, Birmohan Kaur, Manpreet Bhai Sangat Singh Khalsa Coll Dept Comp Sci Banga Punjab India St Longowal Inst Engn & Technol Dept Comp Sci & Engn Longowal Punjab India St Longowal Inst Engn & Technol Dept Elect & Instrumentat Engn Longowal Punjab India

Epilepsy is one of the most common neurological disease worldwide. It is diagnosed by analyzing a long electroencephalogram (EEG) recording in a clinical environment, which may be much prone to errors and a time-consuming task. In this paper, a methodology for the classification of an epileptic seizure is proposed for analyzing EEG signals. EEG signal is decomposed into intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). A fusion, of the extracted non-linear and spike-based features from each of the IMF signals, is made. The parameters of five machine learning algorithms;k-nearest neighbor (k-NN), extreme learning machine (ELM), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) are optimized, as well as a set of the significant features is chosen using grasshopper optimization algorithm (GOA). These classifiers with their optimized parameters are ensembled together for the classification of epileptic seizures. The results show that ensemble classifier performs better than individual classifier. A comparison of the proposed methodology with state of the art epileptic seizure detection techniques is also made for validation.

关键词： Epilepsy Intrinsic mode functions Empirical mode decomposition k-Nearest neighbor Extreme learning machine Random forest Support vector machine Artificial neural network grasshopper optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

grasshopper optimization algorithm optimized multistage controller for automatic generation control of a power system with FACTS devices

引用

Protection and Control of Modern Power Systems 2021年第1期6卷 109-123页

作者： Pratap Chandra Nayak Ramesh Chandra Prusty Sidhartha Panda Department of Electrical Engineering VSSUTBurlaOdisha768018India

This paper uses a grasshopper optimization algorithm (GOA) optimized PDF plus (1 + PI) controller for Automatic generation control (AGC) of a power system with Flexible AC Transmission system (FACTS) devices. Three differently rated reheat turbine operated thermal units with appropriate generation rate constraint (GRC) are considered along with different FACTS devices. A new multistage controller design structure of a PDF plus (1 + PI) is introduced in the FACTS empowered power system for AGC while the controller gains are tuned by the GOA. The superiority of the proposed algorithm over the Genetic algorithm (GA) and Particle Swarm optimization (PSO) algorithms is demonstrated. The dynamic responses of GOA optimized PDF plus (1 + PI) are compared with PIDF, PID and PI controllers on the same system. It is demonstrated that GOA optimized PDF plus (1 + PI) controller provides optimum responses in terms of settling time and peak deviations compared to other controllers. In addition, a GOA-tuned PDF plus (1 + PI) controller with Interline Power Flow Controller (IPFC) exhibits optimal results compared to other FACTS devices. The sturdiness of the projected controller is validated using sensitivity analysis with numerous load patterns and a wide variation of parameterization. To further validate the real-time feasibility of the proposed method, experiments using OPAL-RT OP5700 RCP/HIL and FPGA based real-time simulations are carried out.

关键词： Automatic generation control FACTS devices Multistage controller grasshopper optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

grasshopper optimization algorithm tuned maximum power point tracking for solar photovoltaic systems

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第9期12卷 8637-8645页

作者： Subramanian, Annapoorani Raman, Jayaparvathy Agni Coll Technol Dept Elect & Elect Engn Chennai 600130 Tamil Nadu India SSN Coll Engn Dept Elect & Commun Engn Chennai 603110 Tamil Nadu India

Solar Photovoltaic (PV) system is an excellent renewable energy solution in today's scenario. Harvesting maximum power from the solar PV system under dynamic meteorological conditions is a challenging task. Numerous bio-inspired Maximum Power Point Tracking (MPPT) strategies have been proposed in the literature. The conventional methods of MPPT control are easy and simple to implement, but has drawbacks such as steady state oscillations and inability to track the maximum power under swiftly varying irradiances and partial shading conditions. This paper proposes a grasshopper optimization algorithm (GOA) tuned MPPT technique with the objective of obtaining optimal duty cycle,D, to control a DC-DC boost converter. The efficacy of the proposed system under start up transients, line disturbances, load disturbances, servo conditions and partial shading conditions are evaluated and compared with the conventional Perturb and Observe (P&O) based MPPT and the familiar Particle Swarm optimization (PSO) based MPPT algorithm using MATLAB Simulink platform. It is observed that the proposed GOA tuned MPPT technique gives good steady state and dynamic response compared to P&O and PSO based MPPT algorithms, verified in terms of rise time, settling time, percentage maximum overshoot, Integral Squared Error and Integral Absolute Error.

关键词： Photovoltaic systems Maximum power point tracking Perturb and observe Particle swarm optimization grasshopper optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

grasshopper optimization algorithm with principal component analysis for global optimization

引用

JOURNAL OF SUPERCOMPUTING 2020年第7期76卷 5609-5635页

作者： Yue, Xiaofeng Zhang, Hongbo Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Jilin Peoples R China

As one of the latest meta-heuristic algorithms, the grasshopper optimization algorithm (GOA) has extensive applications because of its efficiency and simplicity. However, the basic GOA still has enough room for improvement. Therefore, a new variant GOA algorithm which combines two strategies, namely PCA-GOA, is proposed. Firstly, principal component analysis strategy is employed to obtain the grasshoppers with minimally correlated variables, which can improve the exploitation capability of the GOA. Then, a novel inertia weight is proposed to balance exploration and exploitation in an intelligent way, which makes the GOA to have better search capability. Furthermore, the performance of PCA-GOA is evaluated by solving a series of benchmark functions. The experimental results manifest that the PCA-GOA provides better outcomes than the basic GOA and other state-of-the-art algorithms on the majority of functions, which demonstrates the superiority of the PCA-GOA.

关键词： grasshopper optimization algorithm Principal component analysis Novel inertia weight Global optimization

来源：评论

学校读者我要写书评

暂无评论

grasshopper optimization algorithm: Theory, Variants, and Applications

引用

IEEE ACCESS 2021年 9卷 50001-50024页

作者： Meraihi, Yassine Gabis, Asma Benmessaoud Mirjalili, Seyedali Ramdane-Cherif, Amar Univ MHamed Bougara Boumerdes Lab LIST Boumerdes Algeria Ecole Natl Super Informat Lab Methodes Concept Syst Algiers Algeria Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab YFL Seoul 03722 South Korea Univ Versailles St Quentin Yvelines LISV Lab F-78140 Velizy Villacoublay France

grasshopper optimization algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

关键词： optimization Particle swarm optimization Genetic algorithms Licenses MIMICs Education Economics grasshopper optimization algorithm GOA meta-heuristics optimization population-based algorithm swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

grasshopper optimization algorithm With Crossover Operators for Feature Selection and Solving Engineering Problems

引用

IEEE ACCESS 2022年 10卷 23304-23320页

作者： Ewees, Ahmed A. Gaheen, Marwa A. Yaseen, Zaher Mundher Ghoniem, Rania M. Damietta Univ Dept Comp Dumyat 34511 Egypt Univ Southern Queensland Sch Math Phys & Comp USQs Adv Data Analyt Res Grp Darling Hts Qld 4350 Australia Al Ayen Univ Sci Res Ctr New Era & Dev Civil Engn Res Grp Thi Qar 64001 Iraq Univ Teknol MARA Kompleks Al Khawarizmi Inst Big Data Analyt & Artificial Intelligence IB Shah Alam 40450 Selangor Malaysia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 11671 Saudi Arabia Mansoura Univ Dept Comp Mansoura 35516 Egypt

Feature selection (FS) is an irreplaceable phase that makes data mining more efficient. It effectively enhances the implementation and decreases the computational problem of learning models. The comprehensive and greedy algorithms are not suitable for the present growing number of features when detecting the optimal subset. Thus, swarm intelligence algorithms (SI) are becoming more common in dealing with FS problems. The grasshopper optimizer algorithm (GOA) represents a new SI;it showed good performance in different fields. Another promising nature-inspired algorithm is a salp swarm algorithm, denoted as SSA, an SI used to tackle optimization issues. In this paper, two phases are applied to propose a new method using crossover-salp swarm with grasshopper optimization algorithm (cSG). In this method, the crossover operators are used to maintain the population of the SSA then the improved SSA is used as a local search to boost the exploration phase of the GOA. Subsequently, this improvement prevents the cSG from premature convergence, high computation time, and being trapped in local minimum. To confirm the effectiveness of proposed cSG method, it is evaluated in different optimizations problems. Eventually, the obtained results are compared to a number of well-known algorithms over global optimization, feature selection datasets, and six real-engineering problems. Experimental results point out that the cSG is superior in solving different optimization problems due to the integration of crossover operators and SSA which enhances its performance and flexibility.

关键词： optimization Feature extraction Training Statistics Sociology Search problems Particle swarm optimization grasshopper optimization algorithm crossover operator salp swarm algorithm optimization problems feature selection engineering problems

来源：评论

学校读者我要写书评

暂无评论

grasshopper optimization algorithm-Based PI Controller Scheme for Performance Enhancement of a Grid-Connected Wind Generator

引用

JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2020年第2期31卷 393-401页

作者： Amin, Mina N. Soliman, Mahmoud A. Hasanien, Hany M. Abdelaziz, Almoataz Y. Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Petr Marine Serv Co Dynam Positioning & Nav Dept Alexandria 22776 Egypt Future Univ Egypt Fac Engn & Technol Cairo Egypt

Wind power installation has been rapidly increasing around the globe, which resulted in a massive penetration into the electric grids. So, tremendous attempts are exerted to improve the behaviour of the grid-tied permanent magnet synchronous generator driven by a variable-speed wind turbine (PMSG-VSWT). This study exhibits the application of the grasshopper optimization algorithm-based proportional-integral (PI) controller with the purpose of enhancing the dynamic and transient stability performances of the grid-tied PMSG-VSWT. The simulation-based optimization method is utilized in the optimization process, where the integration of square error criterion is considered as a fitness function. The effectiveness of the proposed control technique is compared with Newton-Raphson algorithm-based PI controllers, taking into account subjecting the system to symmetrical and unsymmetrical fault conditions. For achieving practical responses, real wind speed data that extracted from Zafarana wind farm in Egypt are utilized in this study. The feasibility of the proposed control approach is validated by the simulation studies, which are accomplished by using MATLAB/Simulink environment.

关键词： Frequency converter Permanent-magnet synchronous generator Variable-speed wind turbine grasshopper optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：