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Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling

WATER RESOURCES MANAGEMENT 2021年第12期35卷 4127-4147页

作者： Ahmadi, Farshad Mehdizadeh, Saeid Mohammadi, Babak Shahid Chamran Univ Ahvaz Dept Hydrol Water Resources Ahvaz Iran Urmia Univ Water Engn Dept Orumiyeh Iran Lund Univ Dept Phys Geog & Ecosyst Sci Solvegatan 12 SE-22362 Lund Sweden

The present study aimed to model reconnaissance drought index (RDI) time series at three various time scales (i.e., RDI-6, RDI-9, RDI-12). Two weather stations located at Iran, namely Tehran and Dezful, were selected as the case study. First, support vector regression (SVR) was utilized as the standalone modeling technique. Then, hybrid models were implemented via coupling the standalone SVR with two bio-inspired-based techniques including firefly algorithm (FA) and whale optimization algorithm (WOA) as well as wavelet analysis (W). Accordingly, the hybrid SVR-FA, SVR-WOA, and W-SVR models were proposed. It is worth mentioning that six mother wavelets (i.e., Haar, Daubechies (db2, db4), Coifflet, Symlet, and Fejer-Korovkin) were employed in development of the hybrid W-SVR models. The performance of models was assessed through root mean square error (RMSE), mean absolute error (MAE), Willmott index (WI), and Nash-Sutcliffe efficiency (NSE). Generally, the implemented coupled models illustrated better results than the standalone SVR in modeling the RDI time series of studied locations. Besides, the Coifflet mother wavelet was found to be the best-performing wavelet. The most accurate results were achieved for RDI-12 modeling via the W-SVR utilizing db4(2) at Tehran station (RMSE = 0.253, MAE = 0.174, WI= 0.888, NSE = 0.934) and Coifflet(2) at Dezful station (RMSE = 0.301, MAE = 0.166, WI= 0.910, NSE = 0.936). As a result, the hybrid models developed in the current study, specifically W-SVR ones, can be proposed as suitable alternatives to the single SVR.

关键词： Reconnaissance drought index Support vector regression Firefly algorithm whale optimization algorithm Wavelet Hybrid models

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A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass

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SOFT COMPUTING 2021年第17期25卷 11877-11895页

作者： Li, Zimu Bejarbaneh, Behnam Yazdani Asteris, Panagiotis G. Koopialipoor, Mohammadreza Armaghani, Danial Jahed Tahir, M. M. Southwest Univ Sch Comp & Informat Sci Chongqing 400715 Peoples R China Univ Teknol Malaysia Fac Civil Engn Dept Geotech & Transportat Skudai 81310 Johor Malaysia Sch Pedag & Technol Educ Computat Mech Lab Athens 14121 Greece Amirkabir Univ Technol Fac Civil & Environm Engn Tehran 15914 Iran Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia Univ Teknol Malaysia UTM Construct Res Ctr Sch Civil Engn Inst Smart Infrastruct & Innovat Construct ISI Johor Baharu 81310 Johor Malaysia

The efficiency of tunnel boring machines (TBMs) in underground projects has great significance for the mining and tunneling industries, demanding a reliable estimation of the TBM's performance in different geotechnical conditions. The current research work attempted to suggest an optimal predictor model of TBM performance as a reliable alternative to experimental and numerical techniques. To achieve this target, three data-mining techniques, namely neural network (NN), gene expression programming (GEP), and multivariate adaptive regression splines (MARS), were employed for modelling the TBM performance, and then the most robust predictive model was optimized via a metaheuristic search method known as whale optimization algorithm (WOA). For the modelling purpose, an experimental database was compiled by performing a field assessment program in a tunneling project in Malaysia and then conducting laboratory testing on the derived rock specimens. Based on the measured experimental data, the six most influential parameters were identified and served as model inputs to predict penetration rate (PR). In order to indicate the capability of the developed GEP and NN models, a stepwise linear regression model, i.e., MARS, was designed for PR prediction as well. The predictive capacity of the constructed models was quantified using a series of statistical indices, i.e., root mean squared error (RMSE), determination coefficient (R-2) and variance account for (VAF). Based on the computed indices for testing records, both the proposed GEP and NN models (with RMSE values of 0.1882 and 0.2120 and R-2 values of 0.9058 and 0.8735, respectively) yielded more accurate predictive results than the MARS model with RMSE of 0.2553 and R-2 of 0.8346. Hence, by achieving the most robust performance compared to the rest, GEP-based model can provide a new practical equation with a high level of accuracy. In other part of this study, the six input parameters of the GEP model and its equation were

关键词： TBM performance Penetration rate Gene expression programming Neural network Multivariate adaptive regression splines whale optimization algorithm

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A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design

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NEURAL COMPUTING & APPLICATIONS 2020年第18期32卷 14701-14718页

作者： Mohammed, Hardi Rashid, Tarik Charmo Univ Coll Med & Appl Sci Appl Comp Dept Chamchamal Krg Iraq Sulaimani Polytech Univ Tech Coll Informat Sulaimani Krg Iraq Univ Kurdistan Hewler UKH Sch Comp & Engn Comp Sci & Engn Hewler Krg Iraq

A recent metaheuristic algorithm, such as whale optimization algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey wolf optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase, while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions, and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO, and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and fitness-dependent optimizer (FDO).

关键词： whale optimization algorithm Grey wolf optimization Benchmark test functions Nature-inspired algorithms Engineering problem Solving pressure vessel design

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A novel speech emotion recognition model using mean update of particle swarm and whale optimization-based deep belief network

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DATA TECHNOLOGIES AND APPLICATIONS 2020年第3期54卷 297-322页

作者： Rajasekhar, B. Kamaraju, M. Sumalatha, V Gudlavalleru Engn Coll Gudlavalleru Krishna India Jawaharlal Nehru Technol Univ Anantapur India

Purpose Nowadays, the speech emotion recognition (SER) model has enhanced as the main research topic in various fields including human-computer interaction as well as speech processing. Generally, it focuses on utilizing the models of machine learning for predicting the exact emotional status from speech. The advanced SER applications go successful in affective computing and human-computer interaction, which is making as the main component of computer system's next generation. This is because the natural human machine interface could grant the automatic service provisions, which need a better appreciation of user's emotional states. Design/methodology/approach This paper implements a new SER model that incorporates both gender and emotion recognition. Certain features are extracted and subjected for classification of emotions. For this, this paper uses deep belief network DBN model. Findings Through the performance analysis, it is observed that the developed method attains high accuracy rate (for best case) when compared to other methods, and it is 1.02% superior to whale optimization algorithm (WOA), 0.32% better from firefly (FF), 23.45% superior to particle swarm optimization (PSO) and 23.41% superior to genetic algorithm (GA). In case of worst scenario, the mean update of particle swarm and whale optimization (MUPW) in terms of accuracy is 15.63, 15.98, 16.06% and 16.03% superior to WOA, FF, PSO and GA, respectively. Under the mean case, the performance of MUPW is high, and it is 16.67, 10.38, 22.30 and 22.47% better from existing methods like WOA, FF, PSO, as well as GA, respectively. Originality/value This paper presents a new model for SER that aids both gender and emotion recognition. For the classification purpose, DBN is used and the weight of DBN is used and this is the first work uses MUPW algorithm for finding the optimal weight of DBN model.

关键词： Emotion recognition Deep belief network whale optimization algorithm Styling Gender recognition

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Temperature-sensitive point selection and thermal error modeling of spindle based on synthetical temperature information

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2021年第3-4期113卷 1029-1043页

作者： Li, Zheyu Li, Guolong Xu, Kai Tang, Xiaodong Dong, Xin Chongqing Univ State Key Lab Mech Transmiss Chongqing Peoples R China

Thermal errors are the important factors that affect machining accuracy. It is effective to improve the accuracy by establishing the error model and compensating for the errors. In this paper, a temperature-sensitive point selection method based on synthetical temperature information (STI) is proposed, and the thermal error model using support vector regression (SVR) optimized by the whale optimization algorithm (WOA) is established. Firstly, the STI matrix is constructed by combining the temperature value information, the temperature shape information, and the relationship between temperatures and errors. Multiple cluster validity indexes (CVI) are used to determine the optimal number of clusters. Secondly, the STI matrix and the optimal number of clusters are used for fuzzy C-means clustering (FCM), and the correlation coefficient is used to select the temperature-sensitive points. Thirdly, the SVR model optimized by the WOA based on STI (S-WOA-SVR) is established. Finally, the S-WOA-SVR model is verified at different speeds and compared with other traditional methods, such as the SVR model optimized by the WOA based on the traditional clustering method (T-WOA-SVR) and the SVR model optimized by the GA based on STI (S-GA-SVR). The results show that the S-WOA-SVR model has higher accuracy and robustness. Compared with the T-WOA-SVR model and the S-GA-SVR model, the RMSE of the S-WOA-SVR model is reduced by 52.3% and 46.6% for the thermal elongation in Z direction delta(z), respectively.

关键词： Thermal error Synthetical temperature information whale optimization algorithm Support vector regression Cluster validity index

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Analytical study on optimized configuration strategy of electrochemical energy storage system under multiple scenarios

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Li, Tou Zhang, Wei Zhang, Xuan School of Electrical Engineering Nanjing Institute of Technology Jiangsu Nanjing210000 China Nanjing Weilixuan Information Technology Co. Ltd Jiangsu Nanjing210000 China

This paper models the electrochemical energy storage system and proposes a control method for three aspects, such as battery life, to generate a multiobjective function for optimizing the capacity allocation of electrochemical energy storage under multiple scenarios, with conditional constraints on the system, storage, and progression aspects. The improved whale optimization algorithm is used to solve the multi-objective function to find the most reasonable electrochemical energy storage system capacity optimization allocation scheme. Using the model constructed in this paper under multi-scenario conditions, it is found after solving that the optimal allocation scheme purchases power from the grid at around 25MW during the highest peak hours in summer and 5MW in winter, which ensures the economic benefits. Meanwhile, the maximum power fluctuation of the electrochemical energy storage system at point A of the optimization strategy provided by the model is only 2.16%, which is much lower than the preset 4.32%, so the optimal allocation strategy reaches the optimum. Comparing the performance of configured energy storage in different scenarios, the peak-valley power difference of the model proposed in this paper decreases from 11.6 MW to 8.9 MW, which is a better performance than that of the control group, which is 10.8 MW-9.1 MW, and the effect of peak shaving and valley filling is obvious. © 2024 Tou Li, Wei Zhang and Xuan Zhang, published by Sciendo.

关键词： Electrochemical energy storage Multi-objective function Optimal allocation whale optimization algorithm

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WOANet: whale optimized deep neural network for the classification of COVID-19 from radiography images

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BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2021年第4期41卷 1702-1718页

作者： Murugan, R. Goel, Tripti Mirjalili, Seyedali Chakrabartty, Deba Kumar Natl Inst Technol Silchar Dept Elect & Commun Engn Biomed Imaging Lab BIOMIL Silchar 788010 Assam India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Silchar Med Coll & Hosp Dept Radiol Silchar 788014 Assam India

Coronavirus Diseases (COVID-19) is a new disease that will be declared a global pandemic in 2020. It is characterized by a constellation of traits like fever, dry cough, dyspnea, fatigue, chest pain, etc. Clinical findings have shown that the human chest Computed Tomography (CT) images can diagnose lung infection in most COVID-19 patients. Visual changes in CT scan due to COVID-19 is subjective and evaluated by radiologists for diagnosis purpose. Deep Learning (DL) can provide an automatic diagnosis tool to relieve radiologists' burden for quantitative analysis of CT scan images in patients. However, DL techniques face different training problems like mode collapse and instability. Deciding on training hyper parameters to adjust the weight and biases of DL by a given CT image dataset is crucial for achieving the best accuracy. This paper combines the backpropagation algorithm and whale optimization algorithm (WOA) to optimize such DL networks. Experimental results for the diagnosis of COVID-19 patients from a comprehensive COVID-CT scan dataset show the best performance compared to other recent methods. The proposed network architecture results were validated with the existing pre-trained network to prove the efficiency of the network. (c) 2021 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： Early diagnosis COVID-19 whale optimization algorithm Machine learning Deep learning

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Determining Optimum Location and Sizing of Distributed Generation Systems in a Real Radial Distribution Network

ELECTRICA

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ELECTRICA 2021年第3期21卷 342-351页

作者： Kaya, Anil Bozkurt, Altug Yildiz Tech Univ Grad Sch Sci & Engn Dept Elect Engn Istanbul Turkey Yildiz Tech Univ Dept Elect Engn Istanbul Turkey

In recent years, with the changing concept of electrical energy, distributed generation systems have gained more importance. Distributed generation systems can contribute to distribution networks by increasing energy supply security, reducing power losses, and improving voltage stability. Optimal locations and sizes should be determined to obtain maximum benefit from distributed generation systems that will be connected to distribution networks. This paper presents a multi objective placement and sizing optimization model of distributed generation systems to minimize power losses and voltage deviations. The proposed model is implemented on a 40 bus real radial network using particle swarm algorithm and whale optimization algorithm. The obtained results on different load scenarios using particle swarm algorithm and whale optimization algorithm algorithms are presented comparatively.

关键词： Distributed generation systems particle swarm optimization whale optimization algorithm

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End-Point Dynamic Control Model for 260 Tons BOF Steelmaking Based on PWWTSVR and LWOA

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STEEL RESEARCH INTERNATIONAL 2023年第7期94卷

作者： Wang, Miao Li, Shengli Gao, Chuang Ai, Xingang Zhai, Baopeng Univ Sci & Technol Liaoning Sch Mat & Met Anshan 114051 Liaoning Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Liaoning Peoples R China Ansteel Informat Ind Co Ltd Automat Div Smart Steelmaking Business Div Anshan 114000 Liaoning Peoples R China

The control of end-point carbon content and temperature is very important for basic oxygen furnace (BOF) steelmaking. Herein, to improve the control precision of the BOF end-point, a new dynamic control model of the BOF end-point is proposed. First, based on the samples of low-carbon steel collected from the steel plant, a prediction model of BOF end point is established by using projection wavelet weighted twin support vector regression (PWWTSVR). It is indicated in the simulation results that the error bound of carbon content and temperature are 0.005 wt% and 10 degrees C, respectively, and the hit rates of the prediction models are 92% and 90%. Moreover, a double hit rate of 84% is higher than the other five prediction models. Based on the PWWTSVR prediction model, a Levy-flying whale optimization algorithm (LWOA) is used to optimize the objective function to establish the control model. The control model shows that the mean absolute error of the end-blow oxygen volume calculated by the model is 75.0201 Nm(3), which is smaller by comparing the other two control models. The proposed control model can provide efficient guidance for on-site smelting personnel.

关键词： basic oxygen furnaces end-point model twin support vector regression whale optimization algorithm

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Battery SOH estimation and RUL prediction framework based on variable forgetting factor online sequential extreme learning machine and particle filter

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JOURNAL OF ENERGY STORAGE 2023年 65卷

作者： Duan, Wenxian Song, Shixin Xiao, Feng Chen, Yuan Peng, Silun Song, Chuanxue Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China Jilin Univ Sch Mech & Aerosp Engn Changchun 130022 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China

Battery life prediction is of great practical significance to ensure the safety and reliability of equipment. This paper proposes a new framework to realize battery state of health (SOH) estimation and remaining useful life (RUL) prediction. The variable forgetting factor online sequential extreme learning machine (VFOS-ELM) is used to estimate battery SOH, and particle filter (PF) algorithm used to predict battery RUL. To improve the estimation accuracy, a new nonlinear decline method, adaptive weight and Gaussian variation are used to improve the standard whale optimization algorithm (WOA) algorithm. And the improved IWOA algorithm is used for parameter optimization of the VFOS-ELM and PF algorithm. The extremely randomized trees (ERT) algorithm is used to obtain the features with high correlation with the available capacity to reduce the complexity of the model and improve the estimation accuracy. Compared with other methods, the proposed IWOA-VFOS-ELM algorithm has higher estimation performance and noise anti-interference ability. The MAE of APR-3 and APR -4 for SOH estimation are both within 0.12 %, RMSE are within 0.15 %, and IA are both higher than 99.9 %. Compared with PF algorithm, the RUL prediction accuracy obtained by IWOA-PF algorithm is improved by 7.143 %, 6.445 % and 15.094, respectively. In summary, the IWOA-PF algorithm proposed in this paper can be used to predict the battery RUL, and the prediction performance is better than the PF algorithm.

关键词： State of health Remaining useful life whale optimization algorithm Extremely randomized trees Online sequential extreme learning machine

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