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Cooperative resource sharing and cost minimization in energy hub systems using an improved grasshopper optimization algorithm approach

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 120卷

作者： Fei, Rui Cui, Jianwen Nanjing Univ Finance & Econ Hongshan Coll Dept Finance & Taxat Nanjing 210003 Jiangsu Peoples R China

This study presents a cooperative paradigm for energy hub systems (EHSs) where a network of interconnected hubs cooperates in exploiting the resources with the purpose of economic saving. In such an architecture, each hub provided with various sources of energy, such as combined heat and power (CHP), hot water tanks, renewable sources, electric chillers, and absorption chillers, will integrate all these sources for more adaptability and efficiency to the system. Moreover, the integration of energy storage systems (ESSs) is considered to enhance the flexibility of the energy hub concerning power, heating, and cooling. Recognizing the complexity associated with incorporating multiple constraints, the improved grasshopper optimization algorithm (IGOA) is introduced to effectively address this challenge. By leveraging this algorithm, the study aims to overcome the intricacies involved in considering various constraints and achieve an optimal outcome. The IGOA improves the efficiency and effectiveness of local and national searches in solving complex energy hub optimization problems. Reducing the likelihood of getting stuck in suboptimal solutions, enhances the algorithm's ability to find optimal solutions considering multiple constraints, thereby enhancing the overall performance and cost-effectiveness of EHSs. The issue is defined as a planning challenge, and by collaborative efforts, the expenses associated with the network energy hubs are reduced, illustrating the efficacy of this concept. The findings indicate the influence of the suggested cooperative technique, with operating cost reductions of 19.09 %, 13.27 %, and 8.75 % for Hub 1, Hub 2, and Hub 3, respectively. Furthermore, the cooperative framework eradicates energy deficits and disruptions, in contrast to 1,198.21 kWh of unfulfilled demand and 22 interruptions in the non-cooperative scenario. These results underscore the significant advantages of the collaborative technique in improving cost-efficiency, reli

关键词： Energy hub systems Cooperative framework Resource sharing Cost minimization improved grasshopper optimization algorithm

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An improved grasshopper optimization algorithm for Global optimization

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Chinese Journal of Electronics 2021年第3期30卷 451-459页

作者： YAN Yan MA Hongzhong LI Zhendong College of Energy and Electrical Engineering Nanjing Hohai University School of Information Engineering Ningxia University Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education Ningxia University

We proposes an improved grasshopper algorithm for global optimization problems. grasshopper optimization algorithm(GOA) is a recently proposed meta-heuristic algorithm inspired by the swarming behavior of grasshoppers. The original GOA has some drawbacks,such as slow convergence speed, easily falling into local optimum, and so on. To overcome these shortcomings, we proposes a grasshopper optimization algorithm based on a logistic Chaos maps opposition-based learning strategy and cloud model inertia weight(CCGOA). CCGOA is divided into three stages. The chaos opposition learning initialization strategy is used to initialize the population,so that the population can be evenly distributed in the feasible solution space as much as possible, so as to improve the uniformity and diversity of the initial population distribution of the grasshopper algorithm. The inertia weight cloud model is introduced into the grasshopper algorithm, and different inertia weight strategies are used to adjust the convergence speed of the algorithm. Based on the principle of chaotic logistic maps, local depth search is carried out to reduce the probability of falling into local optimum. Fourteen benchmark functions and an engineering example are used for simulation *** results show that the proposed CCGOA algorithm has superior performance in determining the optimal solution of the test function problem.

关键词： initial population distribution initialization strategy inertia weight cloud model inertia weight strategies improved grasshopper optimization algorithm CCGOA algorithm search problems particle swarm optimisation global optimization problems convergence meta-heuristic algorithm learning (artificial intelligence) cloud model inertia weight logistic chaos maps opposition-based learning strategy optimal solution chaos

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An evolutionary stacked generalization model based on deep learning and improved grasshopper optimization algorithm for predicting the remaining useful life of PEMFC

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APPLIED ENERGY 2023年第PartA期330卷

作者： Zhang, Chu Hu, Haowen Ji, Jie Liu, Kang Xia, Xin Nazir, Muhammad Shahzad Peng, Tian Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Huaiyin Inst Technol Jiangsu Permanent Magnet Motor Engn Res Ctr Huaian 223003 Peoples R China Suqian Univ Sch Mech & Elect Engn Suqian 223800 Peoples R China

Accurate prediction of the future degradation trend (FDT) and remaining useful life (RUL) of proton exchange membrane fuel cell (PEMFC) is crucial in the prognosis and health management (PHM) of PEMFC. Therefore, this study develops a stacked generalization model (SGM) to predict the FDT and RUL of the PEMFC. Firstly, maximum information coefficient (MIC) and Akaike information criterion (AIC) are used to select input variables to obtain key information and reduce the difficulty of model training. Then use improved grasshopper optimization algorithm (GOA) to optimize the hyperparameters of the SGM to further improve the prediction performance of the model. Finally, according to the dataset after feature selection, improved GOA (IGOA) optimized SGM (M/AIC-IGOA-SGM) is established to predict FDT and RUL. Among them, IGOA is obtained by introducing Chebyshev chaotic mapping initialization, chaotic decreasing factor, and a spiral position update mechanism to GOA. The base models of SGM are support vector regression (SVR) and gated recurrent unit (GRU), and the meta-model is regularized extreme learning machine (RELM). In the comparative experiments under two different current conditions, the superiority of the proposed model is verified, and the effectiveness of M/AIC, IGOA, and SGM is discussed.

关键词： Proton exchange membrane fuel cell Future degradation trend Remaining useful life Gated recurrent unit improved grasshopper optimization algorithm Stacked generalization

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Development and application of an evolutionary deep learning framework of LSTM based on improved grasshopper optimization algorithm for short-term load forecasting

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JOURNAL OF BUILDING ENGINEERING 2022年 57卷

作者： Hu, Haowen Xia, Xin Luo, Yuanlin Zhang, Chu Nazir, Muhammad Shahzad Peng, Tian Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Suqian Coll Sch Mech & Elect Engn Suqian 223800 Peoples R China PowerChina Huadong Engn Corp Ltd Hangzhou 310000 Peoples R China

Accurate short-term load forecasting (STLF) plays an important role in the daily operation of a smart grid. In order to forecast short-term load more effectively, this article proposes an integrated evolutionary deep learning approach based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), improved grasshopper optimization algorithm (IGOA), and long short-term memory (LSTM) network. First of all, CEEMDAN is used to decompose the original data into a certain number of periodic intrinsic mode functions (IMFs) and a residual. Secondly, the nonlinear strategy is used to improve the attenuation coefficient of GOA, and the golden sine operator is introduced to update the individual position of GOA. Then the improved GOA is used to optimize the parameters of the LSTM model, which are the number of hidden neurons and learning rate. The optimized LSTM is applied to the decomposed modal components. Finally, the prediction results of each modal component are aggregated to get the real STLF results. Through comparative experiments, the effectiveness of the CEEMDAN method, the IGOA method, and the combined model is verified, respectively. The experimental results show that the integrated evolutionary deep learning method proposed in this article is an effective tool for STLF.

关键词： Short-term load forecasting Complete ensemble empirical mode decomposition with adaptive noise improved grasshopper optimization algorithm Long short-term memory

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A hybrid short-term load forecasting model developed by factor and feature selection algorithms using improved grasshopper optimization algorithm and principal component analysis

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ELECTRICAL ENGINEERING 2020年第1期102卷 437-460页

作者： Salami, Mesbaholdin Sobhani, Farzad Movahedi Ghazizadeh, Mohammad Sadegh Islamic Azad Univ Dept Ind Engn Cent Tehran Branch Tehran Iran Islamic Azad Univ Dept Ind Engn Sci & Res Branch Tehran Iran Shahid Beheshti Univ Abbaspour Sch Engn Dept Elect Engn Tehran Iran

Hybrid load forecasting models analyze linear and nonlinear components separately. If hybrid models were integrated with factor and feature selection algorithms, they would improve significantly. In the hybrid model proposed by this paper, the initial data were decomposed by an empirical mode decomposition (EMD) model. The linear component was analyzed through the autoregressive integrated moving average (ARIMA) method and the nonlinear component by a neural network (NN) and weighted by the improved flower pollination algorithm (IFPA). With the nonlinear component, the input load demand variable was decomposed by a wavelet transform (WT). In this paper, the improved grasshopper optimization algorithm (IGOA) and the principal component analysis (PCA) were employed to determine the input feature and input factor, respectively. Therefore, the proposed model was composed of EMD, IGOA, PCA, ARIMA, IFPA, NN, and WT algorithms. Finally, Iran's Electricity Market (IEM) data were used to show improvements in the precision of the proposed forecasting model.

关键词： Empirical mode decomposition improved flower pollination algorithm improved grasshopper optimization algorithm Neural network Principal component analysis Short-Term Load Forecasting

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Parameter identification of photovoltaic cell model based on improved grasshopper optimization algorithm

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OPTIK 2021年 247卷 167979-167979页

作者： Wu, Zhongqiang Shen, Dandan Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao Hebei Peoples R China

An accurate mathematical model has practical applications in the design and research of solar cells. Many intelligent optimization algorithms are currently used to extract solar cell parameters. However, optimization algorithms including grasshopper optimization algorithm (GOA) are easy to fall into local optimums. An improved grasshopper optimization algorithm (IGOA) was proposed in this paper. Chaotic initialization was carried out to improve the quality of initial population. Then differential evolution strategy was introduced to maintain the diversity of the population through mutation, crossover and selection process, leading to getting rid of local optimums and searching for better solutions in GOA. To accelerate the convergence speed of the algorithm, the positions of particles were updated with current particle optimal values as targets as in particle swarm optimization. The optimization experiments on standard test functions shown the superiority of IGOA compared with other intelligent optimization algorithms. IGOA was then used to identify the parameters of polycrystalline silicon solar cells. The identification accuracy and stability of IGOA are much higher than harris hawks optimization, grey wolf optimization and ant lion optimization. The effectiveness of IGOA in identifying parameters of solar cell under different illuminations was also verified by experiments.

关键词： Photovoltaic cell Parameter identification improved grasshopper optimization algorithm Differential evolution algorithm Chaotic initialization

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A local spectrum enhancement-based method and its application in incipient fault diagnosis of rotating machinery

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International Journal of Mechanical System Dynamics 2023年第2期3卷 162-172页

作者： Jiancong Shi Baoming Xu Xinglong Wang Jun Zhang School of Mechanical Engineering and Automation Fuzhou UniversityFuzhouChina

Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis *** address this challenge,this paper proposes an effective local spectrum enhancement‐based diagnostic method that can identify weak fault frequencies in the original complicated raw *** this purpose,a traversal frequency band segmentation technique is first proposed for dividing the raw signal into a series of subfrequency ***,the proposed synthetic quantitative index is constructed for selecting the most informative local frequency band(ILFB)containing fault features from the divided subfrequency ***,an improved grasshopper optimization algorithmbased stochastic resonance(SR)system is developed for enhancing weak fault features contained in the selected most ILFB with less computation ***,the enhanced weak fault frequencies are extracted from the output of the SR system using a common spectrum *** experiments on a laboratory planetary gearbox and an open bearing data set are used to verify the effectuality of the proposed *** diagnostic results demonstrate that the proposed method can identify incipient faults of gears and bearings in an effective and accurate ***,the advantages of the proposed method are highlighted by comparison with other methods.

关键词： fault diagnosis frequency band segmentation adaptive stochastic resonance improved grasshopper optimization algorithm synthetic quantitative index

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IGHOA Based Modified Convolutional Neural Network for Prediction of Cardiovascular Disease

IGHOA Based Modified Convolutional Neural Network for Predic...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Dileep, M.R. Jaiswal, Riyansh Vishwanatha, C.R. Thatikonda, Ramya Avula, Sashikanth Reddy Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India University of the Cumberlands Department of Information Technology CharlotteNC United States

ISBN: (纸本)9798350313413

Prediction of heart disease is more crucial for reducing mortality rates. Many other conditions can contribute to heart disease, including clogged arteries, heart attacks, chest discomfort, and strokes. Bypass surgery or coronary artery surgery is performed to correct issues with the heart's muscles, valves, or rhythm caused by heart disease. Lack of early diagnosis of heart disease in humans is a leading cause of mortality. Using a pre-processing and classification strategy, accurately forecast cardiac issues. In this study, we utilise the UCI heart disease dataset and a real-time dataset to evaluate deep learning algorithms against conventional approaches. As a first step, we examined the interplay and dependency between a variety of clinical characteristics and coronary heart disease. Finally, we use both the whole set of features and a smaller subset to conduct an in-depth examination of the Modified Convolutional Neural Network (MCNN) classification model. In order to enhance the CNN's classification capabilities, an IGHOA model is applied to it. Accuracy, the ROC curve, and the F1-Score were used to classifiers. The classification outcomes demonstrated that pertinent characteristics had a significant effect on organization precision. When compared to replicas trained on a complete feature set, the presentation of classification models trained with a smaller feature set better melodramatically with less training period. © 2023 IEEE.

关键词： Correlation dependence Deep Learning Heart Disease improved grasshopper optimization algorithm Modified Convolutional Neural Network

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ONBLR: An effective optimized ensemble ML approach for classifying liver cirrhosis disease

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 89卷

作者： Badvath, Dhanalaxmi Miriyala, Aruna Safali Gunupudi, Sai Chaitanya Kumar Kuricheti, Parish Venkata Kumar Malla Reddy Inst Engn & Technol Autonomous Dept Comp Sci & Engn Hyderabad Telangana India Dhanekula Inst Engn & Technol Dept Comp Sci & Engn Vijayawada Andhra Prades India DVR & Dr HS MIC Coll Technol Dept Artificial Intelligence Ponnavaram Andhra Prades India Velagapudi Ramakrishna Siddhartha Engn Coll Dept Comp Applicat Vijayawada Andhra Prades India

Liver cirrhosis is a late stage of scarring (fibrosis) of the liver caused by many forms of liver diseases and conditions, such as hepatitis and chronic alcoholism. Early diagnosis and management can help slow the progression of the disease and improve quality of life. Developing more accurate and non-invasive methods for the early detection of liver cirrhosis to enhance timely intervention and improve patient outcomes. Effective prediction reduces overall cirrhosis patients' mortality due to various bleeding, making further study in this field a significant task. Therefore, we propose a novel ensemble machine learning based approach for classification. Label encoding and data normalization using the min-max approach are performed in preprocessing. Then, characteristics such as age, gender, Liver function tests (AST, ALT, alkaline phosphatase, bilirubin) and Medical history and comorbidities are retrieved using the ConvNeXt approach. Using the improved grasshopper optimization algorithm (IGOA), the essential features that increase the accuracy even more were selected. Finally, the optimized ensemble machine learning approach naive Bayes and logistic regression (ONBLR) is employed to classify the liver cirrhosis disease. Harris Hawks optimization algorithm is employed to optimize the hyperparameters. The proposed approach is compared with the existing state-of-the-art machine learning approaches. While compared with existing approaches, the proposed model yields 99.18 % accuracy, 99.12 % of Sensitivity and 98.92 % of specificity which is greater among other approaches.

关键词： Liver disease improved grasshopper optimization algorithm ConvNeXt approach Naive Bayes Logistic regression Harris Hawks optimization algorithm

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Structural damage detection based on modal strain energy assurance criterion using adaptive region shrinkage assisted IGOA

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STRUCTURES 2023年 58卷

作者： Chen, Zepeng Liu, Qitian Pan, Chudong Foshan Univ Sch Transportat & Civil Engn & Architecture Foshan Peoples R China Guangzhou Univ Sch Civil Engn Guangzhou Peoples R China Jinan Univ KOE Key Lab Disaster Forecast & Control Engn Guangzhou Peoples R China

The efficiency in high-dimension optimization, premature convergence and noise robustness are common challenges that restrict the application of swarm-based algorithms in structural damage detection (SDD). In order to overcome these limitations, an improved grasshopper optimization algorithm (IGOA) based SDD approach assisted with adaptive region shrinkage strategies is proposed using modal strain energy assurance criterion. Firstly, the effectiveness of GOA in handling SDD is investigated, and an improved version is subsequently introduced with two adaptive region shrinkage strategies: adaptive Le ' vy flight mechanism and elite oppositionbased learning, aiming to achieve more accurate and stable SDD results. Additionally, a novel objective function is defined, combining the frequency change ratio (FCR) and modal strain energy assurance criterion (MSEAC). To assess the performance of the proposed algorithm, a comparative analysis is conducted with four other selected swarm intelligent algorithms and GOAs with individual modification using four CEC benchmark functions. The results demonstrate that the proposed algorithm achieves improved convergence rate and accuracy. Furthermore, the effectiveness and efficiency of the proposed algorithm in SDD are validated through two numerical simulations and an experimental test. The SDD results obtained from both numerical simulations and experimental tests confirm the accuracy and robustness of the proposed method.

关键词： Structural damage detection (SDD) improved grasshopper optimization algorithm (IGOA) Modal strain energy assurance criterion (MSEAC) Adaptive Le ' vy flight Elite opposition-based learning

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