检索结果-内蒙古大学图书馆

2nd Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Zhang, Huanlong Gao, Zeng Zhang, Jie Yang, Guanglu Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou Peoples R China China Tobacco Henan Ind Co Ltd Nanyang Cigarette Factory Nanyang Peoples R China

ISBN: (纸本)9783030316532;9783030316549

grasshopper optimization algorithm (GOA) is a new meta-heuristic optimization algorithm that it simulates behavior of grasshopper swarms in nature. In this paper, a tracking framework called improved levy flight grasshopper optimization algorithm (LGOA) tracker is proposed. The levy flight can increase the diversity of population, prevent premature convergence and enhance the capability of jumping out of local optimal optima, thus improving the tracking accuracy. In addition, GOA has been applied to visual tracking for the first time as far as we know. Finally, compared with other optimization-based trackers, experimental results show that our tracker has obvious advantages.

关键词： Visual tracking grasshopper optimization algorithm Levy flight Local optimal

来源：评论

学校读者我要写书评

暂无评论

The Harris hawks optimization algorithm, salp swarm algorithm, grasshopper optimization algorithm and dragonfly algorithm for structural design optimization of vehicle components

MATERIALS TESTING

引用

MATERIALS TESTING 2019年第8期61卷 744-748页

作者： Yildiz, Betul Sultan Yildiz, Ali Riza Uludag Univ Dept Automot Engn Gorukle Bursa Turkey Univ Michigan Ann Arbor MI 48109 USA Mississippi State Univ NSF & DOE Funded Res Projects Starkville MS USA

There is a growing interest in designing lightweight and low-cost vehicles. In this research, the Harris hawks optimization algorithm (the HHO), the salp swarm algorithm (SSA), the grasshopper optimization algorithm(GOA), and the dragonfly algorithm (DA) are introduced to solve shape optimization problems in the automotive industry. This research is the first application of the HHO, the SSA, the GOA, and the DA to shape design optimization problems in the literature. In this paper, the HHO, the SSA, and the DA algorithms are used for shape optimization of a vehicle brake pedal to prove how the HHO, the SSA, the GOA, and the DA can be used for solving shape optimization problems. The results show the ability of the HHO, the SSA, the GOA, and the DA to design better optimal components.

关键词： Harris hawks algorithm salp swarm algorithm grasshopper optimization algorithm dragonfly algorithm structural optimization

来源：评论

学校读者我要写书评

暂无评论

Distributed energy resource allocation using multi-objective grasshopper optimization algorithm

引用

ELECTRIC POWER SYSTEMS RESEARCH 2021年 201卷

作者： Ahmadi, Bahman Ceylan, Oguzhan Ozdemir, Aydogan Istanbul Tech Univ Dept Elect Engn Istanbul Turkey Kadir Has Univ Management & Informat Syst Dept Istanbul Turkey

The penetration of small-scale generators (DGs) and battery energy storage systems (BESSs) into the distribution grid is growing rapidly and reaching a high percentage of installed generation capacity. These units can play a significant role in achieving various objectives if installed at suitable locations with appropriate sizes. In this paper, we present a new multi-objective optimization model to improve voltage profiles, minimize DG and BESS costs, and maximize energy transfer between off-peak and peak hours. We allocate and size DG and BESS units to achieve the first two objectives, while optimizing the operation strategy of BESS units for the last objective. The Multi-Objective grasshopper optimization algorithm (MOGOA) is used to solve the formulated constrained optimization problem. The proposed formulation and solution algorithm are tested on 33-bus and 69-bus radial distribution networks. The advantages of the Pareto solutions are discussed from various aspects, and the Pareto solutions are subjected to cost analysis to identify the best solutions in the context of the worst voltage profiles at peak load times. Finally, the performance of the MOGOA algorithm is compared with the other heuristic optimization algorithms using two Pareto optimality indices.

关键词： Distribution network planning Optimal planning Photovoltaic generation Wind energy generation Battery energy storage system grasshopper optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A Novel Network Architecture of Decision-Making for Self-Driving Vehicles Based on Long Short-Term Memory and grasshopper optimization algorithm

引用

IEEE ACCESS 2020年 8卷 155429-155440页

作者： Shi, Yunxia Li, Ying Fan, Jiahao Wang, Tan Yin, Taiqiao Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China Space Technol Jilin Co Ltd Jilin 132013 Jilin Peoples R China Jilin Univ Coll Software Changchun 130012 Peoples R China

Long short-term memory network is one of the most important network architectures of decision-making for self-driving vehicles. Nevertheless, the decision-making accuracy of long short-term memory network is limited, the information of the surrounding vehicles is not taken into consideration, which is critical for the decision-making of the ego vehicle, and the classification capability of long short-term memory network is poor. In this article, a novel network architecture called improved long short-term memory network with support vector machine classifier optimized by grasshopper optimization algorithm (GOA-ImLSTM) is proposed. Three improvements are presented in GOA-ImLSTM. Firstly, to consider the information of the surrounding vehicles, a new network architecture, used to extract vital features for self-driving vehicles, with three parallel long short-term memory network units and a network unit serial connected according to vehicle location is designed. Secondly, to improve classification accuracy, support vector machine with stronger classification capability than softmax is introduced to accomplish the classification task. Thirdly, to promote the classification capability of support vector machine, grasshopper optimization algorithm is employed to optimize the parameters of support vector machine. Moreover, to balance exploration and exploitation ability of grasshopper optimization algorithm, dynamic weights in position movement formula are defined. The experiments indicate that GOA-ImLSTM improves the accuracy of results compared with other decision-making methods for self-driving vehicles on the Next Generation SIMulation.

关键词： Support vector machines Decision making optimization Feature extraction Logic gates Hidden Markov models Classification algorithms grasshopper optimization algorithm long short-term memory self-driving decision-making support vector machine

来源：评论

学校读者我要写书评

暂无评论

Improving grasshopper optimization algorithm for hyperparameters estimation and feature selection in support vector regression

引用

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2021年 208卷

作者： Algamal, Zakariya Yahya Qasim, Maimoonah Khalid Lee, Muhammad Hisyam Ali, Haithem Taha Mohammad Univ Mosul Dept Stat & Informat Mosul Iraq Univ Mosul Dept Gen Sci Mosul Iraq Univ Teknol Malaysia Fac Sci Dept Math Sci Johor Baharu Malaysia Nawroz Univ Coll Comp & Informat Technol Dahuk Kurdistan Regio Iraq

High-dimensionality is one of the major problems which affect the quality of the classification and prediction modeling. Support vector regression has been applied in several real problems. However, it is usually needed to tune manually the *** addition, SVR cannot perform feature selection. Nature-inspired algorithms have been used as a feature selection and as hyperparameters estimation procedure. In this paper, an improving grasshopper optimization algorithm (GOA) is proposed by adapting a new function of the main controlling parameter of GOA to enhance the exploration and exploitation capability of GOA. This improving is utilized to optimize the hyperparameters of the SVR with embedding the feature selection simultaneously. Experimental results, obtained by running on four datasets, show that our proposed algorithm performs better than crossvalidation method, in terms of prediction, number of selected features, and running time. Besides, the experimental results of the proposed improving confirm the efficiency of the proposed algorithm in improving the prediction performance and computational time compared to other nature-inspired algorithms, which proves the ability of GOA in searching for the best hyperparameters values and selecting the most informative features for prediction tasks.

关键词： grasshopper optimization algorithm Evolutionary algorithm SVR Feature selection

来源：评论

学校读者我要写书评

暂无评论

Non-linear Channel Equalization using Modified grasshopper optimization algorithm

引用

APPLIED SOFT COMPUTING 2024年 153卷

作者： Ingle, Kishor Kisan Jatoth, Ravi Kumar Natl Inst Technol Warangal Dept Elect & Commun Engn Warangal 506004 Telangana India

In this paper, a modified grasshopper optimization algorithm is proposed for equalization of non-linear wireless communication channels. Even though grasshopper optimisation algorithm (GOA) is an efficient algorithm, it gets trapped into local optima after some iterations due to loss of swarm diversity. Furthermore, GOA does not involve any provision to retain the elite grasshoppers found so far at each index which weakens the exploitation capability and convergence rate of GOA. These limitations of GOA are alleviated in this paper by incorporating three key modifications into GOA. A threshold parameter is introduced to detect the inefficient search region. Le ' vy Flight is integrated with the basic GOA to improve the diversity of grasshopper swarm and the greedy selection operator is used to preserve the elite grasshoppers found so far at every index. The superiority of a modified grasshopper optimization algorithm (MGOA) is illustrated over the existing metaheuristic algorithms. The key parameters of MGOA are selected by performing the sensitivity analysis. The simulation results on four non-linear channels demonstrate the equalization capability of the proposed MGOA in terms of MSE and BER performance. A statistical validity of results provided by MGOA is confirmed through Wilcoxon rank -sum test.

关键词： Channel equalization Metaheuristic algorithms grasshopper optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Enhanced grasshopper optimization algorithm with extreme learning machines for motor-imagery classification

引用

ASIAN JOURNAL OF CONTROL 2023年第4期25卷 3015-3028页

作者： Balmuri, Kavitha Rani Madala, Srinivasa Rao Divakarachari, Parameshachari Bidare de Prado, Rocio Perez Frnda, Jaroslav CMR Tech Campus Dept Informat Technol Hyderabad Telangana India PACE Inst Technol & Sci Dept Comp Sci & Engn Ongole Andhra Pradesh India Nitte Meenakshi Inst Technol Dept Elect & Commun Engn Bengaluru Karnataka India Univ Jaen Telecommun Engn Dept Jaen Spain Univ Zilina Dept Quantitat Methods & Econ Informat Zilina Slovakia Nitte Meenakshi Inst Technol Dept Elect & Commun Engn Bengaluru 560064 Karnataka India

In Brain Computer Interface (BCI), achieving a reliable motor-imagery classification is a challenging task. The set of discriminative and relevant feature vectors plays a crucial role in classification. In this article, an enhanced optimization technique is implemented for selecting active feature vectors to enhance motor-imagery classification using Electroencephalography (EEG) signals. After collecting the input EEG signals from BCI competition III-4a and IV-2a databases, the 6th-order butter-worth filter is employed for eliminating base-line wander noise from the raw EEG signals. Further, the Variational Mode Decomposition technique is applied for separating the important signal components from the composite EEG signals, and then, the Higher Order Statistic, kurtosis, skewness, standard deviation, and entropy are utilized for feature extraction. The high-dimensional feature values are given to the Enhanced grasshopper optimization algorithm for optimum feature selection, which are given to the Extreme Learning Machines (ELM) classifier for motor-imagery classification. Finally, in the resulting section, the optimized ELM model achieved 99.48% and 99.12% of accuracy on the BCI competition III-4a and IV-2a databases, where the achieved results are maximum compared to the traditional deep learning models.

关键词： brain computer interface electroencephalography extreme learning machines grasshopper optimization algorithm variational mode decomposition

来源：评论

学校读者我要写书评

暂无评论

Integrating grasshopper optimization algorithm with Local Search for Solving Data Clustering Problems

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2021年第1期14卷 783-793页

作者： El-Shorbagy, M. A. Ayoub, A. Y. Prince Sattam Bin Abdulaziz Univ Coll Sci & Humanities Al Kharj Dept Math Al Kharj 11942 Saudi Arabia Menoufia Univ Fac Engn Shebin Kom Dept Basic Engn Sci Shibin Al Kawm Egypt

This paper proposes a hybrid approach for solving data clustering problems. This hybrid approach used one of the swarm intelligence algorithms (SIAs): grasshopper optimization algorithm (GOA) due to its robustness and effectiveness in solving optimization problems. in addition, a local search (LS) strategy is applied to enhance the solution quality and access to optimal data clustering. The proposed algorithm is divided into two stages, the first of which aims to use GOA to prevent getting trapped in local minima and to find an approximate solution. While the second stage aims by LS to increase LS performance and obtain the best optimal solution. In other words, the proposed algorithm combines the exploitation capability of GOA and the discovery capability of LS, and integrates the merits of both GOA and LS. In addition, 7 well-known datasets that commonly used in several studies are used to validate the proposed technique. The results of the proposed methodology are compared to previous studies;where statistical analysis, for the various algorithms, indicated the superiority of the proposed methodology over other algorithms and its ability to solve this type of problem. (C) 2021 The Authors. Published by Atlantis Press B.V.

关键词： Data clustering problems grasshopper optimization algorithm local search optimization swarm intelligence algorithms

来源：评论

学校读者我要写书评

暂无评论

A grasshopper optimization algorithm-Based Response Surface Method for Non-Probabilistic Structural Reliability Analysis with an Implicit Performance Function

引用

BUILDINGS 2022年第7期12卷

作者： Li, Qi Wang, Junmu Su, Guoshao Guangxi Polytech Construct Sch Civil Engn Nanning 530007 Peoples R China Guangxi Univ Coll Civil Engn & Architecture Key Lab Disaster Prevent & Struct Safety Minist Educ Nanning 530004 Peoples R China Guangxi Univ Guangxi Prov Engn Res Ctr Water Secur & Intellige Nanning 530004 Peoples R China

Non-probabilistic reliability analysis has great developmental potential in the field of structural reliability analysis, as it is often difficult to obtain enough samples to construct an accurate probability distribution function of random variables based on probabilistic theory. In practical engineering cases, the performance function (PF) is commonly implicit. Monte Carlo simulation (MCS) is commonly used for structural reliability analysis with implicit PFs. However, MCS requires the calculation of thousands of PF values. Such calculation could be time-consuming when the structural systems are complicated, and numerical analysis procedures such as the finite element method have to be adopted to obtain the PF values. To address this issue, this paper presents a grasshopper optimization algorithm-based response surface method (RSM). First, the method employs a quadratic polynomial to approximate the implicit PF with a small set of the actual values of the implicit PF. Second, the grasshopper optimization algorithm (GOA) is used to search for the global optimal solution of the scaling factor of the convex set since the problem of solving the reliability index is transformed into an unconstrained optimal problem. During the search process in the GOA, a dynamic response surface updating strategy is used to improve the approximate accuracy near the current optimal point to improve the computing efficiency. Two mathematical examples and two engineering structure examples that use the proposed method are given to verify its feasibility. The results compare favorably with those of MCS. The proposed method can be non-invasively combined with finite element analysis software to solve non-probabilistic reliability analysis problems of structures with implicit PF with high efficiency and high accuracy.

关键词： non-probabilistic reliability analysis response surface method grasshopper optimization algorithm finite element analysis

来源：评论

学校读者我要写书评

暂无评论

An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第3期35卷

作者： Srivel, R. Kalaiselvi, K. Shanthi, S. Perumal, Uma Adhiparasakthi Engn Coll Dept CSE Melmaruvathur Tamil Nadu India SRM Inst Sci & Technol Dept Networking & Commun Chennai India Malla Reddy Coll Engn & Technol Dept CSE Hyderabad India Jazan Univ Jazan Saudi Arabia

Due to the technical words employed, which are primarily recognized by medical specialists, information retrieval in the medical area is sometimes described as sophisticated. Because of this, users frequently have trouble coming up with queries utilizing these medical phrases. However, this problem may be readily fixed by an information retrieval system that finds the pertinent terms that fit the user's query and automatically creates a ranking document using these keywords. To enhance the IR performance, the Automatic Query expansion method is applied by appending additional query terms for the medical domain. We propose a novel fuzzy-based grasshopper optimization algorithm (GOA) based on automatic query expansion. This work is mainly focused on filtering the most relevant augmented query by utilizing the synchronization score of IR evidence like normalized term frequency, inverse document frequency, and normalization of document length. The main aim of this work is to identify the medical terms that appropriately match the user's queries. The GOA algorithm ranks the terms based on relevance and then identifies the terms with the maximum synchronization value. The documents formed using the optimal expanded query are classified into three types, namely totally relevant, moderately relevant, and marginally relevant. Besides, the comparison of the proposed work is carried out for different performance metrics like Mean-Average Precision, F-measure, Precision-recall, and Precision rank are evaluated and analyzed by using TREC-COVID, TREC Genomics 2007, and MEDLARs medical datasets for the proposed and some of the state-of-art works. For a total of 60 queries, the proposed model offers an F1-Score of 0.964, 0.959, and 0.968 for the MEDLARS, TREC Genomics, and TREC COVID19 datasets, respectively. The E1-score and Mean Reciprocal Rate (MRR) of the proposed model is 0.8 and 0.9 when evaluated using the TREC COVID19 dataset. Performance analyses show that the proposed app

关键词： accelerated particle swarm optimization COVID fuzzy logic grasshopper optimization algorithm query expansion

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：