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Flood risk assessment of subway stations based on projection pursuit model optimized by whale algorithm: A case study of Changzhou, China

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2023年 98卷

作者： Ju, Weiyi Wu, Jie Cao, Haizhen Kang, Qingchun Ali, Shah Syed Salman Xing, Zhixiang Changzhou Univ Sch Safety Sci & Engn 21 Gehu Middle Rd Changzhou 213164 Jiangsu Peoples R China

With rapid modernization, traffic jams have become a part of people's daily lives. An increasing number of cities have begun to build subways to solve this problem. This study focuses on the flood risk analysis of subway stations, which is often overlooked. However, such disasters cause serious mass casualties and huge property losses. Therefore, this is of great concern. This study applies the method of projection pursuit model optimized by whale algorithm to evaluate the flood disasters of subway stations. The results showed that the flood risk level of 11 subway stations was very low, that of 21 subway stations was low, that of seven subway stations was moderate, and that of one subway station was high. Compared to the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, the application of the projection pursuit model based on the whale algorithm has higher precision and better global searchability. The main contribution of this study is to provide a new method and idea for the field of flood risk assessment of subway stations, and to provide a scientific model and reference for local rail companies. This study used the whale algorithm to optimize the projection pursuit model and applied it to the flood risk assessment of subway stations. This is an innovation in this field that has strong engineering application significance.

关键词： Flood disaster Subway stations whale optimization algorithm Projection pursuit model Random weight

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Air pollutant prediction based on ARIMA-WOA-LSTM model

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ATMOSPHERIC POLLUTION RESEARCH 2023年第6期14卷

作者： Luo, Jun Gong, Yaping Zhejiang Ocean Univ Dept Ocean Engn Equipment Zhoushan 316022 Peoples R China

The problem of air pollution has always plagued people's lives, and the management of air pollution cannot be achieved without the prediction and assessment of the concentration of various pollutants. In this paper, we propose a method to accurately predict air pollutants with the aim of ensuring the efficiency of air pollution management. The proposed ARIMA-WOA-LSTM model uses ARIMA to extract the linear part of air pollution data and output the nonlinear part, while the WOA-LSTM model is used to predict the nonlinear part, where the whale algorithm is used to find the perfect hyperparameters for the LSTM, and the objectives of the search include the number of neurons, model learning rate and batch length. To prove the excellence of the model developed in the article ARIMA-WOA-LSTM is compared with ARIMA-LSTM, CEEMDAN-WOA-LSTM, WOA-LSTM, ARIMA, and LSTM. The results show that ARIMA-WOA-LSTM performs better than other models in three aspects: pollutant prediction accuracy, model prediction accuracy, and prediction stability;the combined model also performs much better than the single model in the three aspects;The whale algorithm is excellent for the search of the five hyperparameters in the LSTM, which is important for the error reduction of the model. ARIMA-WOA-LSTM model has high reference for air pollution management.

关键词： ARIMA-WOA-LSTM Combined model Air pollution prediction whale optimization algorithm

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RCS reduction effect based on transparent and flexible polarization conversion metasurface arrays

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RESULTS IN PHYSICS 2023年 52卷

作者： Zhen, Qiutong Mao, Zhoutao Cui, Jiayi Guo, Meijia Chernogor, Leonid F. Jin, Zhejun Zheng, Yu Liu, Tian Qingdao Univ Coll Elect & Informat Qingdao 266071 Peoples R China Kharkov Natl Univ Dept Space Radio Phys UA-61022 Kharkiv Ukraine

In recent years, radar cross section (RCS) reduction techniques based on electromagnetic absorption materials have become a hot research topic in the field of electromagnetic stealth. This paper proposes a transparent conformal encoded metasurface based on polarization rotation units. The metasurface array is optimized using the whale optimization algorithm to construct a 1-bit polarization rotation encoded unit array. The proposed metasurface array exhibits good RCS reduction and electromagnetic wave polarization conversion performance in the frequency range of 13.2-20.7 GHz. The RCS reduction effect of the metasurface array is effective at different angles and polarization states. The metasurface is composed of transparent and flexible materials, namely ITO, PET, and PVC plastic. It exhibits conformal properties while maintaining a high physical transmittance of up to 83.6%. The RCS reduction performance of the metasurface is studied under different curvatures and demonstrates good conformal scattering characteristics. The RCS variation of ITO material compared to traditional metallic materials was also analyzed. It was found that ITO with low surface resistance is an effective means to broaden the RCS bandwidth. This makes it suitable for theoretical research and the design of electromagnetic stealth for electronic devices such as radars and antennas with different curvatures.

关键词： Transparent and flexible metasurface Polarization conversion Radar cross section whale optimization algorithm

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Research on the efficient performance algorithm of fragmented blockchain for industrial Internet of Things applications 23

Research on the efficient performance algorithm of fragmente...

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Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering

作者： Yanbo Yin Jianming Zhao Tianyu Wang Weiyan Tong School of Chemical Process Automation Shenyang University of Technology China Key Laboratory of Networked Control Systems Chinese Academy of Sciences China

ISBN: (纸本)9798400708305

This article addresses the challenge of optimizing performance in a fragmentation blockchain system for industrial Internet of things (IIOT) applications. It introduces an enhanced version of the traditional whale optimization algorithm (WOA) known as the Chaotic whale optimization algorithm. This enhanced algorithm is designed to optimize the parameters of the fragmentation blockchain system with the goal of maximizing real-time memory utilization in the fragmentation nodes while minimizing block consensus time. The proposed method identifies optimal blockchain system parameters within diverse network environments, taking into account factors such as node computing power and inter-node transmission rates. To mitigate the issue of local optima inherent in traditional algorithms, the Chaotic whale optimization algorithm transforms the optimization problem in a more intuitive manner. This algorithm strikes a fine balance between diversity and convergence. The effectiveness of this method is assessed through simulation experiments, with results indicating its superior performance compared to alternative approaches. Overall, the approach presented in this article significantly reduces consensus time within the fragmentation blockchain system and enhances real-time memory utilization in the fragmented blocks.

关键词： consensus time industrial Internet of Things real-time utilization of node memory whale optimization algorithm

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Reinforcement Learning Based whale Optimizer 21st

Reinforcement Learning Based Whale Optimizer

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21st International Conference on Computational Science and Its Applications (ICCSA)

作者： Becerra-Rozas, Marcelo Lemus-Romani, Jose Crawford, Broderick Soto, Ricardo Cisternas-Caneo, Felipe Embry, Andres Trujillo Molina, Maximo Arnao Tapia, Diego Castillo, Mauricio Misra, Sanjay Rubio, Jose-Miguel Pontificia Univ Catolica Valparaiso Valparaiso Chile Covenant Univ Ota Nigeria Univ Bernardo OHiggins Santiago Chile Pontificia Univ Catolica Chile Sch Civil Construct Santiago Chile

ISBN: (纸本)9783030870133;9783030870126

This work proposes a Reinforcement Learning based optimizer integrating SARSA and whale optimization algorithm. SARSA determines the binarization operator required during the metaheuristic process. The hybrid instance is applied to solve benchmarks of the Set Covering Problem and it is compared with a Q-learning version, showing good results in terms of fitness, specifically, SARSA beats its Q-Learning version in 44 out of 45 instances evaluated. It is worth mentioning that the only instance where it does not win is a tie. Finally, thanks to graphs presented in our results analysis we can observe that not only does it obtain good results, it also obtains a correct exploration and exploitation balance as presented in the referenced literature.

关键词： Metaheuristic SARSA Q-Learning Swarm intelligence whale optimization algorithm Combinatorial optimization

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The Arithmetic optimization algorithm

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021年 376卷 113609-113609页

作者： Abualigah, Laith Diabat, Ali Mirjalili, Seyedali Elaziz, Mohamed Abd Gandomi, Amir H. Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan New York Univ Abu Dhabi Div Engn Abu Dhabi 129188 U Arab Emirates NYU Tandon Sch Engn Dept Civil & Urban Engn Brooklyn NY 11201 USA Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld Australia Yonsei Univ YFL Yonsei Frontier Lab Seoul South Korea Zagazig Univ Dept Math Fac Sci Zagazig Egypt Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia

This work proposes a new meta-heuristic method called Arithmetic optimization algorithm (AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics including (Multiplication (M), Division (D), Subtraction (S), and Addition (A)). AOA is mathematically modeled and implemented to perform the optimization processes in a wide range of search spaces. The performance of AOA is checked on twenty-nine benchmark functions and several real-world engineering design problems to showcase its applicability. The analysis of performance, convergence behaviors, and the computational complexity of the proposed AOA have been evaluated by different scenarios. Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms. Source codes of AOA are publicly available at http://***/matlabcentral/fileexchange/8474 and https://***/projects. (C) 2020 The Author(s). Published by Elsevier B.V.

关键词： Arithmetic optimization algorithm AOA algorithm optimization Meta-heuristics Heuristic Artificial Intelligence Grey Wolf Optimizer whale optimization algorithm Genetic algorithm Particle Swarm optimization Benchmark Computational Intelligence

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Combined Terrestrial Evapotranspiration Index prediction using a hybrid artificial intelligence paradigm integrated with relief algorithm-based feature selection

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 193卷 106687-106687页

作者： Jamei, Mehdi Elbeltagi, Ahmed Maroufpoor, Saman Karbasi, Masoud Jamei, Mozhdeh Jalali, Mohammadnabi Najafzadeh, Negin Shahid Chamran Univ Ahvaz Shohadaye Hoveizeh Campus Technol Fac Engn Dashte Azadegan Iran Mansoura Univ Fac Agr Agr Engn Dept Mansoura 35516 Egypt Zhejiang Univ Coll Environm & Resource Sci Hangzhou 310058 Peoples R China Univ Tehran Dept Irrigat & Reclamat Engn Tehran Iran Univ Zanjan Fac Agr Zanjan Iran Ferdowsi Univ Mashhad Water Sci & Engn Dept Mashhad Razavi Khorasan Iran Khuzestan Water & Power Author Ahvaz Iran Univ Tehran Dept Water Engn Aburaihan Campus Tehran Iran Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran

Drought is a common environmental disaster strongly influenced by the potential production of agricultural products, lack of water resources, and yields destructive effects on the economy. In this study, the prediction of a novel monthly Combined Terrestrial Evapotranspiration Index (CTEI) was considered as a measure of all three types of drought (meteorology, hydrology, and agriculture) in the Ganga river basin (GRB) over the period of 14 years. For this purpose, a combination of hydro-meteorological and satellite-based data, including 11 input variables, was implemented. A new Artificial Neural Network (ANN) integrated with a whale optimization algorithm (WOA-ANN) and a Relief algorithm-based Feature Selection (FS) method was applied to simulate the monthly CTEI index and find the optimum input combinations. Besides, the standalone ANN and Least Square Support Vector Regression (LSSVR) models were examined to validate the WOA-ANN performance. The results indicated that WOA-ANN with (R = 0.9391), (RMSE = 0.241), (WI = 0.968), and (U-95% = 0.669) had a high ability to predict CTEI and reduced the RMSE in the ANN and LSSVR models by 27% and 30%, respectively. WOA-ANN had the best predictive performance, followed by LSSVR and ANN models, respectively, concerning various graphical validations and diagnostic analyses. Besides, the outcome of this research, despite fewer parameters (seven parameters), considerably outperformed the study of (Elbeltagi et al., 2021) with (R = 0.9055 and RMSE = 0.33) accuracy employing 11 inputs.

关键词： Terrestrial Evapotranspiration Index Drought whale optimization algorithm Relief algorithm ANN LSSVR

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A novel metaheuristic approach to scale the economic impact of grid participation on a microgrid system

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartA期53卷

作者： Dey, Bishwajit Garcia Marquez, Fausto Pedro Panigrahi, Pratap Kumar Bhattacharyya, Biplab IIT ISM Dept Elect Engn Dhanbad Bihar India GIET Univ Dept Elect & Elect Engn Gunupur Odisha India Univ Castilla La Mancha Ingenium Res Grp Ciudad Real Spain

Microgrids are having more importance in the power system network, favouring the use of renewable energy sources (RES), thus, reducing the emission of the toxic gases. Grid-connected mode of microgrid operations is emphasized due to its economic and technical advantages, resulting in diminished generation cost of the system. Six scenarios of grid participation are studied in this paper, with the generation cost of the microgrid system being minimized in each scenario. These scenarios are modelled in a way such that they exhibit various paradigms of connections and disconnections between the grid and microgrid system. A novel hybrid of whale optimization algorithm (WOA) and sine-cosine algorithm (SCA) is proposed as the optimization tool to minimize the generation cost for all the scenarios. Costs obtained by the approach are analyzed with the costs given by differential evolution, whale optimization and sine-cosine algorithms. The generation cost surged approximately 4%, 7% and 11% when the grid is disconnected from the microgrid system for 8, 10 and 12 h respectively. The generation cost, during passive participation of the grid, is as high as 7.5% more than the ideal case. Numerical results and non-parametric statistical analysis such as Friedman and Quade tests, validate the results obtained by the novel hybrid approach.

关键词： Grid participation Microgrid Energy management whale optimization algorithm Sine cosine algorithm

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A complete model parameter optimization from self-potential data using whale algorithm

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JOURNAL OF APPLIED GEOPHYSICS 2019年 170卷 103825-000页

作者： Abdelazeem, Maha Gobashy, Mohamed Khalil, Mohamed H. Abdrabou, Mohamed NRIAG Cairo Egypt Cairo Univ Fac Sci Geophys Dept Giza Egypt

A new heuristic algorithm to provide a solution to spontaneous potential anomalies resulted from idealized bodies like (sphere, horizontal cylinder, vertical cylinder and multiple bodies) is presented. The method is based on utilizing whale optimization algorithm (WOA) as a new solution of the inverse problem of self-potential resulted from simple geometric source bodies. The studied parameters are polarization amplitude (K), the zero distance from origin (D), the depth (h), the polarization angle (theta) and the shape factor (q). The WOA first applied on synthetic example;the effect of random noise is examined as well, and the method depicted good results. The technique is then applied on five real field profiles from Turkey, USA and Indonesia. The inversion results illustrate that WOA accurately detects model parameters, and shows good validation when compared with other inversion methods in the published literature. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Spontaneous-Potential whale optimization algorithm Artificial intelligence Inversion Simple bodies Field studies WOA

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Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data

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GEOCARTO INTERNATIONAL 2023年第1期38卷 Article: 2158951页

作者： Adnan, Rana Muhammad Dai, Hong-Liang Mostafa, Reham R. Islam, Abu Reza Md. Towfiqul Kisi, Ozgur Heddam, Salim Zounemat-Kermani, Mohammad Guangzhou Univ Sch Econ & Stat Guangzhou Peoples R China Mansoura Univ Fac Comp & Informat Sci Informat Syst Dept Mansoura Egypt Begum Rokeya Univ Dept Disaster Management Rangpur Bangladesh Tech Univ Lubeck Dept Civil Engn Lubeck Germany Ilia State Univ Civil Engn Dept Tbilisi Georgia Hydraul Div Univ Fac Sci Agron Dept Skikda Algeria Shahid Bahonar Univ Kerman Dept Water Engn Kerman Iran

The accurate assessment of groundwater levels is critical to water resource management. With global warming and climate change, its significance has become increasingly evident, particularly in arid and semi-arid areas. This study compares new extreme learning machines (ELM) methods tuned with metaheuristic algorithms such as particle swarm optimization, grey wolf optimization, the whale optimization algorithm (WOA), Harris Hawks optimizer (HHO), and the jellyfish search optimizer (JFO) in groundwater level estimation. Daily precipitation and temperature datasets acquired from two stations in northern Bangladesh were used as inputs to the models, which were evaluated based on different quantitative statistics and assessed based on RMSE, MAE, R-2, and some new graphical inspection methods. The outcomes of the applications revealed that the efficiency of ELM models was considerably improved by using metaheuristic algorithms. The ELM-JSO improved the RMSE of the standalone ELM model by 13% for the optimal precipitation, temperature, and groundwater level inputs in the testing stage. Among the implemented methods, the ELM-JFO performed the best in estimating the daily groundwater level, and the ELM-WOA and ELM-HHO, respectively, followed it. Viability of a new extreme machine learning (ELM) method tuned with Jellyfish search optimizer (JFO) is investigated in groundwater level estimation. The ELM-JFO is compared with hybrid ELM-PSO, ELM-WOA and ELM-HHO models using daily precipitation and temperature data acquired from two stations of Bangladesh. The ELM-JSO improves the root mean square error of the standalone ELM model by 13% for the optimal precipitation, temperature and groundwater level inputs.

关键词： Groundwater prediction extreme learning machine particle swarm optimization grey wolf optimization whale optimization algorithm Harris Hawks optimizer jellyfish search optimizer

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