检索结果-内蒙古大学图书馆

Multi-objective optimization of hybrid energy systems using gravitational search algorithm

SCIENTIFIC REPORTS 2025年第1期15卷 1-20页

作者： Mahmoudi, Sayyed Mostafa Maleki, Akbar Ochbelagh, Dariush Rezaei Amirkabir Univ Technol Tehran Polytech Dept Energy Engn & Phys Tehran Iran Shahrood Univ Technol Fac Mech Engn Shahrood Iran

The depletion of fossil fuel reserves, increasing environmental concerns, and energy demands of remote communities have increased the acceptance of using hybrid renewable energy systems (HRES). However, choosing an optimal HRES from economic, environmental, reliability, and sustainability aspects is still challenging. To solve this challenge, this study introduces a novel multi-objective optimization approach using the gravitational search algorithm (GSA) and non-dominated sorting techniques. The proposed framework addresses four objectives: minimizing the loss of power supply probability, reducing total costs, increasing renewable energy fraction, and lowering CO2 emissions. A carbon tax sensitivity analysis evaluates the system's economic performance under varying scenarios. Also, the amount of damage caused by the release of carbon dioxide on human health and the ecosystem is examined. In this way, an optimal configuration consisting of wind turbines, photovoltaic panels, and diesel generators is introduced to satisfy the above objectives. Results demonstrate that the GSA outperforms established methods, such as multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II in Pareto front diversity and convergence. In this work, the optimal system achieves an 18.4% increase in renewable energy share, reducing ecosystem and human health damage by 14.2%. Notably, with a 20% increase in the carbon tax, system costs increased by 3%. These findings underscore the potential of multi-objective optimization combined with carbon tax policies to enhance energy system sustainability and affordability.

关键词： Hybrid renewable energy system Multi-objective optimization gravitational search algorithm Sustainable energy Carbon tax

来源：评论

学校读者我要写书评

暂无评论

Blind CFO estimation based on weighted subspace fitting criterion with fuzzy adaptive gravitational search algorithm

引用

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2024年第1期2024卷 5页

作者： Shen, Chih-Chang Zhang, Ming-Hua Ling Tung Univ Dept Informat Technol Taichung 408 Taiwan

This paper deals with the blind carrier frequency offset (CFO) estimation based on weighted subspace fitting (WSF) criterion with fuzzy adaptive gravitational search algorithm (GSA) for the interleaved orthogonal frequency-division multiplexing access (OFDMA) uplink system. For the CFO estimation problem, it is well known that the WSF has superior statistical characteristics and better estimation performance. However, the type of CFO estimation must pass through the high-dimensional space problem. Optimizing complex nonlinear multimodal functions requires a large computational load, which is difficult and not easy to maximize or minimize nonlinear cost functions in large parameter spaces. This paper firstly presents swarm intelligence (SI) optimization algorithms such as GSA, particle swarm optimization (PSO), and hybrid PSO and GSA (PSOGSA) to improve estimation accuracy and reduce the computational load of search. At the same time, this paper also integrates a fuzzy inference system to WSF-GSA to dynamically adjust the gravitational constant, which can not only reduce the searching computational load, but also improve the performance of GSA in the global optimization and solution accuracy. Finally, several simulation results are provided for illustrating the effectiveness of the proposed estimator.

关键词： Carrier frequency offset Interleaved OFDMA Uplink Weighted subspace fitting gravitational search algorithm Particle swarm optimization Hybrid PSO and GSA Exploration and exploitation Fuzzy inference system

来源：评论

学校读者我要写书评

暂无评论

An IoT-enabled Healthcare Framework For Wireless Body Region Networks Using The gravitational search algorithm

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

引用

JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2024年第3期27卷 2168-2176页

作者： Zhang, Peng Zhao, Man Qiqihar Univ Coll Commun & Elect Engn Qiqihar 161006 Heilongjiang Peoples R China

The smart city idea has gained much popularity in recent years because of its potential to enhance urban people's quality of life. The idea encompasses a wide range of fields, including smart community, smart transportation, and smart healthcare. For intelligent decision-making, most smart city services, particularly those in the smart healthcare field, demand analyzing, processing, and real-time sharing of big healthcare data. The current state of the healthcare industry requires the supply of healthcare products and services, increasing its viability for everyone involved. This paper presents a novel healthcare framework based on clustering sensor nodes, where the physical body is separated into three areas: the bottom, top, and intermediate body regions. The regions are clustered using an enhanced LEACH algorithm. Cluster heads are selected using the gravitational search algorithm (GSA). The MATLAB environment is used to evaluate the proposed framework by comparing it to other approaches. Our framework outperforms previous methods in terms of energy consumption and throughput by 20% and 30%, respectively.

关键词： Internet of things Healthcare gravitational search algorithm Clustering Optimization

来源：评论

学校读者我要写书评

暂无评论

gravitational search algorithm: a comprehensive analysis of recent variants

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2021年第5期80卷 7581-7608页

作者： Mittal, Himanshu Tripathi, Ashish Pandey, Avinash Chandra Pal, Raju Jaypee Inst Informat Technol Noida India Malaviya Natl Inst Technol Jaipur Rajasthan India

gravitational search algorithm is a nature-inspired algorithm based on the mathematical modelling of the Newton's law of gravity and motion. In a decade, researchers have presented many variants of gravitational search algorithm by modifying its parameters to efficiently solve complex optimization problems. This paper conducts a comparative analysis among ten variants of gravitational search algorithm which modify three parameters, namely Kbest, velocity, and position. Experiments are conducted on two sets of benchmark categories, namely standard functions and CEC2015 functions, including problems belonging to different categories such as unimodal, multimodal, and unconstrained optimization functions. The performance comparison is evaluated and statistically validated in terms of mean fitness value and convergence graph. In experiments, IGSA has achieved better precision with balanced trade-off between exploration and exploitation. Moreover, triple negative breast cancer dataset has been considered to analysis the performance of GSA variants for the nuclei segmentation. The variants performance has been analysed in terms of both qualitative and quantitive with aggregated Jaccard index as performance measure. Experiments affirm that IGSA-based method has outperformed other methods.

关键词： Optimization algorithm Nature-inspired algorithm gravitational search algorithm

来源：评论

学校读者我要写书评

暂无评论

gravitational search algorithm for Calculating Exciton Binding Energy in Monolayer Transition Metal Dichalcogenides

引用

JOURNAL OF ELECTRONIC MATERIALS 2021年第1期50卷 163-169页

作者： Oloore, Luqman E. Owolabi, Taoreed O. King Fahd Univ Petr & Minerals Phys Dept Laser Res Grp Dhahran 31261 Saudi Arabia Adekunle Ajasin Univ Phys & Elect Dept Akungba Akoko 342111 Ondo State Nigeria

Large excitonic binding energies in monolayers of transition metal dichalcogenides such as molybdenum disulfide (MoS2), molybdenum diselenide (MoSe2), tungsten disulfide (WS2) and tungsten diselenide (WSe2), were calculated using a gravitational search algorithm. The optimized fitness function is based on a two dimensional (2D) effective mass model of excitons, parameterized by first principle calculations, including a suitable treatment of screening. In addition to the ground state, the binding energies of the first few excited states of the exciton were computed, hence the optical transition energies, as a function of principal quantum number n, were obtained for the exciton states. The method was also used to predict the corresponding 2D polarizabilities, and consequently, dielectric constants for the 2D semiconductors. Dependence of the effective dielectric constants on n was also investigated. Our results compare favorably with existing theoretical methods based on density function theory or GW approximation and the Bethe-Salpeter equation. Furthermore, our results are in reasonable agreement with recent experimental measurements.

关键词： Transition metal dichalcogenides gravitational search algorithm binding energy optical transition energy

来源：评论

学校读者我要写书评

暂无评论

Bi-directional Long Short Term Memory Neural Network for Short-Term Traffic Speed Prediction Using gravitational search algorithm

引用

INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS REsearch 2024年第2期22卷 316-327页

作者： Naheliya, Bharti Redhu, Poonam Kumar, Kranti Maharshi Dayanand Univ Dept Math Rohtak 124001 India Dr BR Ambedkar Univ Delhi Sch Liberal Studies Delhi 110006 India

Traffic speed prediction has implications for urban planning, congestion reduction, and intelligent control systems. To maintain a uniform traffic speed and to avoid issues related to traffic, an accurate traffic speed forecast can help in supplying significant information. The capacity to forecast short-term traffic speed is a fundamental part of both Intelligent Transportation System (ITS) and the Internet of Vehicles (IoV). To achieve better accuracy in predicting short-term traffic speed, we introduced a GSA-Bi-LSTM model by optimizing the Bi-directional Long Short-Term Memory (Bi-LSTM) network prediction framework with gravitational search algorithm (GSA) due to its features of fast convergence, great reliability and significant global search ability of parameters. The utilization of the GSA optimization technique is employed to optimize the hyperparameters of the Bi-LSTM model. By making use of the bidirectional properties of Bi-LSTM layers, the model's architecture aims to enhance prediction accuracy and effectively capture the intricate patterns present in the input data. From the analysis of the experimental results, it becomes evident that the convenience provided by our proposed GSA-Bi-LSTM model surpasses that of conventional models in terms of evaluation metrics. Additionally, it is also noted that GSA has superior optimization capabilities than Particle Swarm Optimization (PSO) in terms of optimizing the Bi-LSTM approach for traffic speed forecasting.

关键词： Intelligent transportation system Traffic speed prediction gravitational search algorithm Bi-LSTM network

来源：评论

学校读者我要写书评

暂无评论

Deep transfer learning with gravitational search algorithm for enhanced plant disease classification

引用

HELIYON 2024年第7期10卷 e28967页

作者： Al-Gaashani, Mehdhar S. A. M. Samee, Nagwan Abdel Alkanhel, Reem Atteia, Ghada Abdallah, Hanaa A. Ashurov, Asadulla Muthanna, Mohammed Saleh Ali Univ Elect Sci & Technol China Sch Resources & Environm 4 1st Ring Rd East 2 Sect Chengdu 610056 Sichuan Peoples R China Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Southern Fed Univ Inst Comp Technol & Informat Secur Taganrog 344006 Russia

Plant diseases annually cause damage and loss of much of the crop, if not its complete destruction, and this constitutes a significant challenge for farm owners, governments, and consumers alike. Therefore, identifying and classifying diseases at an early stage is very important in order to sustain local and global food security. In this research, we designed a new method to identify plant diseases by combining transfer learning and gravitational search algorithm (GSA). Two state-of-the-art pretrained models have been adopted for extracting features in this study, which are MobileNetV2 and ResNe50V2. Multilayer feature extraction is applied in this study to ensure representations of plant leaves from different levels of abstraction for precise classification. These features are then concatenated and passed to GSA for optimizing them. Finally, optimized features are passed to Multinomial Logistic Regression (MLR) for final classification. This integration is essential for categorizing 18 different types of infected and healthy leaf samples. The performance of our approach is strengthened by a comparative analysis that incorporates features optimized by the Genetic algorithm (GA). Additionally, the MLR algorithm is contrasted with KNearest Neighbors (KNN). The empirical findings indicate that our model, which has been refined using GSA, achieves very high levels of precision. Specifically, the average precision for MLR is 99.2%, while for KNN it is 98.6%. The resulting results significantly exceed those achieved with GA -optimized features, thereby highlighting the superiority of our suggested strategy. One important result of our study is that we were able to decrease the number of features by more than 50%. This reduction greatly reduces the processing requirements without sacrificing the quality of the diagnosis. This work presents a robust and efficient approach to the early detection of plant diseases. The work demonstrates the utilization of sophisticated comput

关键词： Deep learning Plant disease gravitational search algorithm Convolutional neural network Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Photovoltaic-Based Dual Output DC-DC Converter Using gravitational search algorithm-Tuned PI and Sliding Mode Controllers

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第12期49卷 16299-16317页

作者： Rajamani, M. P. E. Murugappan, Murugappan Prakash, N. B. Nair, Gomesh Trabelsi, Mohamed Natl Engn Coll Dept Elect & Elect Engn Kovilpatti 628503 Tamilnadu India Kuwait Coll Sci & Technol Dept Elect & Commun Engn Intelligent Signal Proc ISP Res Lab Block 4 Doha 13133 Kuwait Vels Inst Sci Technol & Adv Studies Sch Engn Dept Elect & Commun Engn Chennai Tamil Nadu India Univ Malaysia Perlis Ctr Excellence Unmanned Aerial Syst CoEUAS Arau 02600 Perlis Malaysia Univ Sains Malaysia Sch Elect & Elect Engn Campus Gelugor 14300 Pulau Pinang Malaysia

This paper proposes a dual-output DC-DC power conversion system based on Photovoltaic (PV) technology. PV panels are connected to a series compensated Buck-Boost Converter (SCBBC) to harvest solar energy, while a sliding mode controller (SMC) ensures maximum power point tracking (MPPT). During the intermediate phase, a synchronized Buck-Boost Converter (SBBC) topology is used to ensure effective charging and discharging of batteries. Additionally, a PI-SMC hybrid control strategy is applied at the back end to the Super Lift Luo Converter (SLLC) to maintain the load voltage at a desired value. A gravitational search algorithm (GSA)-based PI controller controls the input current, while the output voltage is controlled by the outer loop (SMC). We use a single-loop SMC approach to validate the performance of the proposed dual-loop control scheme. According to the presented results, the dual-loop control scheme demonstrated higher dynamic performance in controlling input current and output voltage.

关键词： PV systems Maximum power point tracking Series compensated buck-boost converter Synchronized buck-boost converter Super lift luo converter Sliding mode control gravitational search algorithm

来源：评论

学校读者我要写书评

暂无评论

gravitational search algorithm based optimized deep learning model with diverse set of features for facial expression recognition

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第2期12卷 1631-1646页

作者： Alenazy, Wael Mohammad Alqahtani, Abdullah Saleh King Saud Univ Dept Self Dev Skills Common First Year Deanship Comp Skills Riyadh Saudi Arabia

Facial expression recognition (FER) is an essential part of effective human-computer interaction and serves as a helpful medium for children and patients who have problems with communication. However, most of the previous studies focus on building a FER model based on supervised and unsupervised approaches. This paper is focused on a semi-supervised deep belief network (DBN) approach to predict the facial expressions from the CK+, Oulu CASIA, MMI, and JAFFE datasets. To achieve accurate classification of the facial expressions, a gravitational search algorithm (GSA) is applied to optimize some parameters in the DBN network. The Histogram oriented gradients (HOG) and 2D-Discrete Wavelet Transform (2D-DWT) are used for feature extraction from the lip, cheek, brow, eye, and furrow patches. The unwanted information present in the image is eliminated using a feature selection approach. The feature extraction is done by the Kernel-principal component analysis to obtain higher-order correlations between input variables and detect non-linear components. The HOG features extracted from the lip patch provides the best performance for accurate facial expression classification. Finally, a comparative analysis to compare the proposed model with different machine learning techniques based on the evaluation criteria. The results demonstrate that DBN-GSA based classifier is more accurate than the rest of the classifiers.

关键词： Facial expression recognition Deep belief network Semi-supervised approach gravitational search algorithm Histogram oriented gradients

来源：评论

学校读者我要写书评

暂无评论

gravitational search algorithm for determining the optimal kinetic parameters of propane pre-reforming reaction

引用

REACTION KINETICS MECHANISMS AND CATALYSIS 2021年第1期132卷 111-122页

作者： Enikeeva, L., V Potemkin, D., I Uskov, S., I Snytnikov, P., V Enikeev, M. R. Gubaydullin, I. M. Novosibirsk State Univ 2 Pirogova St Novosibirsk 630090 Russia Ufa State Petr Technol Univ 1 Kosmonavtov St Ufa 450062 Russia Boreskov Inst Catalysis SB RAS 5 Lavrentiev Ave Novosibirsk 630090 Russia Novosibirsk State Tech Univ 20 Pr K Marksa Novosibirsk 630073 Russia Russian Acad Sci Inst Petrochem & Catalysis 141 Pr Oktyabrya Ufa 450075 Russia

The article describes the gravitational search algorithm and its application to solve the inverse problem of chemical kinetics. It is shown that metaheuristic algorithms have become more and more popular recently. In this regard, it is logical to apply these algorithms to various optimization problems. The optimization problem is formulated on the example of solving the inverse problem of chemical kinetics. The process under study is the pre-reformation of propane into a methane-rich gas on a Ni catalyst, which is an industrially important chemical process. The article briefly describes the algorithm and its pseudocode, and then compares the performance of the gravitational search algorithm with other metaheuristic methods. The algorithm showed adequate results and was applied to solve a specific technological problem. Using this algorithm, the inverse problem of chemical kinetics was solved and the optimal values of the kinetic parameters of the reaction were found. It was proved that the model correctly described the available experimental data. Moreover, the sensitivity analysis of the algorithm parameters was performed.

关键词： Mathematical modeling Inverse problem of chemical kinetics gravitational search algorithm Propane pre-reforming

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：