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Remora whale optimization-based hybrid deep learning for network intrusion detection using CNN features

EXPERT SYSTEMS WITH APPLICATIONS 2022年 210卷

作者： Pingale, Subhash V. Sutar, Sanjay R. Dr Babasaheb Ambedkar Technol Univ Riagad India

Security remains as a key role in this internet world owing to the fast expansion of users on the internet. Numerous existing intrusion detection approaches were introduced by numerous researchers to recognize and identify intruders. Meanwhile, the existing systems failed to achieve satisfactory detection accuracy. Hence, this paper develops a robust intrusion detection model, named Remora whale optimization (RWO)-based Hybrid deep model for detecting intrusions. Here, the input data is pre-processed, and thereafter data transformation is done. With the transformed data, effective CNN features are extracted and feature conversion is performed to convert the features into vector form. Moreover, RV-coefficient is accomplished for performing feature selection process and finally, network intrusions are effectively detected using Hybrid deep model where the Deep Maxout Network and Deep Auto Encoder are used. On the other hand, the training procedure of the Hybrid deep model is carried out using the designed optimization algorithm, named RWO, which is the hybridization of the Remora optimization algorithm (ROA) and whale optimization algorithm (WOA). Furthermore, the devised technique achieved superior performance using the evaluation metrics, such as testing accuracy, precision, recall, and F1score with the higher values of 0.938, 0.920, 0.932, and 0.926, respectively.

关键词： Security Intrusion detection Deep learning Remora optimization algorithm whale optimization algorithm

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Investigating dam reservoir operation optimization using metaheuristic algorithms

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APPLIED WATER SCIENCE 2022年第12期12卷 280-280页

作者： Lai, Vivien Essam, Yusuf Huang, Yuk Feng Ahmed, Ali Najah El-Shafie, Ahmed Univ Tunku Abdul Rahman Fac Engn & Sci Dept Civil Engn Jalan Bandar Sg Long Kajang 43000 Selangor Malaysia Univ Tenaga Nasl UNITEN Dept Civil Engn Kajang 43000 Selangor Malaysia Univ Tenaga Nasl UNITEN Inst Energy Infrastruct IEI Kajang 43000 Selangor Malaysia Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Natl Water & Energy Ctr Al Ain U Arab Emirates

The optimization of dam reservoir operations is of the utmost importance, as operators strive to maximize revenue while minimizing expenses, risks, and deficiencies. Metaheuristics have recently been investigated extensively by researchers in the management of dam reservoirs. But the animal-concept-based metaheuristic algorithm with Levy flight integration approach has not been used at Karun-4. This paper investigates the optimization of dam reservoir operation using three unexplored metaheuristics: the whale optimization algorithm (WOA), the Levy-flight WOA (LFWOA), and the Harris hawks optimization algorithm (HHO). Utilizing a time series data set on the hydrological and climatic characteristics of the Karun-4 hydroelectric reservoir in Iran, an analysis was conducted. The objective functions and constraints of the Karun-4 hydropower reservoir were examined throughout the optimization procedure. HHO produces the best optimal value, the least-worst optimal value, the best average optimal value, and the best standard deviation (SD) with scores of 0.000026, 0.001735, 0.000520, and 0.000614, respectively, resulting in the best overall ranking mean (RM) with a score of 1.5 at Karun-4. Throughout the duration of the test, the optimized trends of water release and water storage indicate that HHO is superior to the other investigated metaheuristics. WOA has the best correlation of variation (CV) with a score of 0.090195, while LFWOA has the best convergence rate (3.208 s) and best CPU time. Overall, it can be concluded that HHO has the most desirable performance in terms of optimization. Yet, current studies indicate that both WOA and LFWOA generate positive and comparable outcomes.

关键词： Dam reservoir operation optimization Metaheuristics whale optimization algorithm Levy-flight whale optimization algorithm Harris hawk optimization

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AC&DC optimal power flow incorporating centralized/decentralized multi-region grid control employing the whale algorithm

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AIN SHAMS ENGINEERING JOURNAL 2021年第2期12卷 1907-1922页

作者： Swief, R. A. Hassan, Nada Mamdouh Hasanien, Hany M. Abdelaziz, Almoataz Youssef Kamh, M. Z. Ain Shams Univ Fac Engn Cairo Egypt Future Univ Egypt Fac Engn & Technol Cairo Egypt

In this paper, different optimization algorithms are introduced to solve the security-constrained optimal power flow between two connected regions, applying two control modes, namely the centralized and decentralized modes. Traditionally, the decentralized control, i.e., each power system controls its variables regardless of the boundary buses, is the most commonly used approach. However, the centralized control has been recently utilized to enhance the system security, because the active power of the generation units and/or the voltage buses on the boundaries are the two variables, which can mitigate the changes, when compared to the decentralized control solution. The optimization techniques applied propose using the Genetic algorithm (GA) as a tool to validate the solution reached through the whale optimization algorithm. The proposed methodology is investigated on the IEEE 48-bus electric power system, which consists of two regions connected to each other through three transmission lines. The problem can be solved by applying both the AC and DC Optimal Power Flow respectively, using three approaches, namely: 1- Changing the active power of generation unit;2- Controlling the voltage magnitude and 3- Combining the changes in the real power of generation units and the voltage variations. All the proposed approaches are implemented taking into consideration the technical constraints. All of these different techniques are constructed and modeled by utilizing the Matlab code. The results of each case study are presented;and a final comparison between all the methods utilized is drawn to demonstrate the advantages of each method. The novelty of this paper is represented in investigating the effectiveness of applying the AC Optimal Power Flow and the DC Power Flow, utilizing meta-heuristic techniques. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.

关键词： AC optimal power flow Centralized technique DC optimal power flow Decentralized technique Genetic algorithm Optimal power flow optimization algorithms whale optimization algorithm

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Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods

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WATER RESOURCES MANAGEMENT 2021年第1期35卷 199-223页

作者： Anaraki, Mahdi Valikhan Farzin, Saeed Mousavi, Sayed-Farhad Karami, Hojat Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan *** Iran

In the present study, for the first time, a new framework is used by combining metaheuristic algorithms, decomposition and machine learning for flood frequency analysis under climate-change conditions and application of HadCM3 (A2 and B2 scenarios), CGCM3 (A2 and A1B scenarios) and CanESM2 (RCP2.6, RCP4.5 and RCP8.5 scenarios) in global climate models (GCM). In the proposed framework, Multivariate Adaptive Regression Splines (MARS) and M5 Model tree are used for classification of precipitation (wet and dry days), whale optimization algorithm (WOA) is considered for training least square support vector machine (LSSVM), wavelet transform (WT) is used for decomposition of precipitation and temperature, LSSVM-WOA, LSSVM, K nearest neighbor (KNN) and artificial neural network (ANN) are performed for downscaling precipitation and temperature, and discharge is simulated under present period (1972-2000), near future (2020-2040) and far future (2070-2100). Log normal distribution is used for flood frequency analysis. Furthermore, analysis of variance (ANOVA) and fuzzy method are employed for uncertainty analysis. Karun3 Basin, in southwest of Iran, is considered as a case study. Results indicated that MARS performed better than M5 model tree. In downscaling, ANN and LSSVM_WOA slightly outperformed other machine learning algorithms. Results of simulating the discharge showed superiority of LSSVM_WOA_WT algorithm (Nash-Sutcliffe efficiency (NSE) = 0.911). Results of flood frequency analysis revealed that 200-year discharge decreases for all scenarios, except CanESM2 RCP2.6 scenario, in the near future. In the near and far future periods, it is obvious from ANOVA uncertainty analysis that hydrological models are one of the most important sources of uncertainty. Based on the fuzzy uncertainty analysis, HadCM3 model has lower uncertainty in higher return periods (up to 60% lower than other models in 1000-year return period).

关键词： Machine learning whale optimization algorithm Wavelet transform Flood frequency Climate change Fuzzy uncertainty

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Thermal Analysis of Conductive-Convective-Radiative Heat Exchangers With Temperature Dependent Thermal Conductivity

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IEEE ACCESS 2021年 9卷 138876-138902页

作者： Khan, Naveed Ahmad Sulaiman, Muhammad Kumam, Poom Abu Bakar, Maharani Abdul Wali Khan Univ Mardan Dept Math Mardan 23200 Khyber Pakhtunk Pakistan King Mongkuts Univ Technol Thonburi KMUTT Fac Sci Dept Math KMUTT Fixed Point Res LabKMUTT Fixed Point Theor Bangkok 10140 Thailand King Mongkuts Univ Technol Thonburi KMUTT Ctr Excellence Theoret & Computat Sci TaCS CoE Bangkok 10140 Thailand China Med Univ China Med Univ Hosp Dept Med Res Taichung 40402 Taiwan Univ Malaysia Terengganu Fac Ocean Engn Technol & Informat Special Interest Grp Modelling & Data Analyt Terengganu 21300 Malaysia

In this paper, one dimensional mathematical model of convective-conductive-radiative fins is presented with thermal conductivity depending on temperature. The temperature field with insulated tip is determined for a fin in convective, conductive and radiative environments. Moreover, an intelligent soft computing paradigm named as the LeNN-WOA-NM algorithm is designed to analyze the mathematical model for the temperature field of convective-conductive-radiative fins. The proposed algorithm uses function approximating ability of Legendre polynomials based on artificial neural networks (ANN's), global search optimization ability of whale optimization algorithm (WOA), and local search convergence of Nelder-Mead algorithm. The proposed algorithm is applied to illustrate the effect of variations in coefficients of convection, radiation heat losses, and dimensionless parameter of thermal conductivity on temperature distribution of conductive-convective and radiative fins in convective and radiative environments. The experimental data establishes the effectiveness of the design scheme when compared with techniques in the latest literature. It can be observed that accuracy of approximate temperature increases with lower values of N-c and N-r while decreases with increase in lambda. The quality of solutions obtained by LeNN-WOA-NM algorithm are validated through performance indicators including absolute errors, MAD, TIC, and ENSE.

关键词： Conductive-convective-radiative fin temperature-dependent thermal conductivity temperature distribution weighted Legendre neural networks hybrid soft computing whale optimization algorithm Nelder Mead algorithm

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A modified nature-inspired meta-heuristic methodology for heterogeneous unmanned aerial vehicle system task assignment problem

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SOFT COMPUTING 2021年第22期25卷 14227-14243页

作者： Fan, Chenchen Han, Song Li, Xinbin Zhang, Tao Yuan, Yi Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China

This study deals with the task assignment problem in a heterogeneous unmanned aerial vehicle system under the multiple constraints of limited resources, effective task execution scope, obstacle avoidance and task priority. Considering the nonlinearity, multimoding and high complexity of the task assignment problem, many existing optimization methods may fall into local optimum and even fail to provide satisfactory solutions. To address the issues, the discrete adaptive search whale optimization algorithm is proposed. First, we present the obstacle-avoiding distance estimation method to provide precise task scenario information and reduce the computational complexity. Then, the crossover-based updating method is presented to enable the algorithm to solve discrete problems such as task assignment. In addition, the search intensity adaptive mechanism is presented to optimize and balance the exploration and exploitation intensities in the solution space reasonably, thereby achieving high-quality task assignment results quickly. Further, joint leadership mechanism and local search mechanism are proposed to enhance the exploration ability of the algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed algorithm.

关键词： Heterogeneous UAV system Task assignment Obstacle avoidance Task priority whale optimization algorithm

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A novel hybrid machine learning model for short-term wind speed prediction in inner Mongolia, China

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RENEWABLE ENERGY 2021年 179卷 1565-1577页

作者： Lin, Boqiang Zhang, Chongchong Xiamen Univ China Inst Studies Energy Policy Sch Management Xiamen 361005 Fujian Peoples R China Innovat Lab Sci & Technol Energy Mat Fujian Prov Xiamen 361101 Fujian Peoples R China

Wind power is recognized as one of the most promising renewable and clean energy sources under the context of the increasing depletion of fossil fuels. The exact wind speed forecasting has great significance for the large-scale connection of wind farms with the power grid. In light of this, this paper contributes to establishing a novel hybrid model that can predict the future wind speed accurately. Firstly, the original wind speed time series is decomposed by the fast ensemble empirical mode decomposition into several sub-series that are further integrated by the runs test. The phase space reconstruction is used to dynamically choose each integrated sub-series' input and output vectors for the prediction model. Additionally, an improved whale optimization algorithm is exploited to optimize the weights and bias of the extreme learning machine. Finally, prediction results are obtained from the aggregation of each integrated sub-series prediction. To verify the accuracy and applicability of the proposed hybrid model, we apply several comparative models to conducted two case studies using different wind speed time series from Inner Mongolia that is Asia's largest gathering area of wind power farms. According to the experimental results, it can be concluded that the decomposition reduces the volatility and randomness of wind speed, and the runs test lowers the forecasting complexity. The phase space reconstruction can capture the chaotic property of wind speed series. The optimization for the whale optimization algorithm enhances its global and local optimization ability to further improve the performance of extreme learning machine. Overall, the proposed hybrid model can effectively capture the non-linear charac-teristics of wind speed series. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Short-term wind speed prediction Fast ensemble empirical mode decomposition Runs test Phase space reconstruction whale optimization algorithm Extreme learning machine

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A hybrid RBF neural network based model for day-ahead prediction of photovoltaic plant power output

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FRONTIERS IN ENERGY RESEARCH 2024年 11卷

作者： Zhang, Qipei Tang, Ningkai Lu, Jixiang Wang, Wei Wu, Lin Kuang, Wenteng NARI Technol Co Ltd NARI Res Inst Nanjing Jiangsu Peoples R China

Renewable energy resources like solar power contribute greatly to decreasing emissions of carbon dioxide and substituting generators fueled by fossil fuels. Due to the unpredictable and intermittent nature of solar power production as a result of solar radiance and other weather conditions, it is very difficult to integrate solar power into conventional power systems operation economically in a reliable manner, which would emphasize demand for accurate prediction techniques. The study proposes and applies a revised radial basis function neural network (RBFNN) scheme to predict the short-term power output of photovoltaic plant in a day-ahead prediction manner. In the proposed method, the linear as well as non-linear variables in the RBFNN scheme are efficiently trained using the whale optimization algorithm to speed the convergence of prediction results. A nonlinear benchmark function has also been used to validate the suggested scheme, which was also used in predicting the power output of solar energy for a well-designed experiment. A comparison study case generating different outcomes shows that the suggested approach could provide a higher level of prediction precision than other methods in similar scenarios, which suggests the proposed method can be used as a more suitable tool to deal such solar energy forecasting issues.

关键词： photovoltaic power plant solar energy forecasting radial basis function neural network whale optimization algorithm low carbon

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Strengthening energy transition through effective urban electric demand prediction: A multidisciplinary exploration of AI, policy, and administrative strategies

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SUSTAINABLE CITIES AND SOCIETY 2024年 107卷

作者： Chang, Qingyu Wu, Jiang Zhang, Yang Hunan Int Econ Univ Business Sch Changsha 410205 Peoples R China Changsha Univ Sch Econ & Management Changsha 410022 Peoples R China Univ Technol & Business Collaborat Innovat Ctr Anticorruput Changsha 410205 Peoples R China

This research endeavors to strengthen energy transition in urban management through the enhanced accuracy of electric load demand forecasting within a multi-faceted approach considering legal and policy decision making associate with the proposed problem. Firstly, a prediction model is devised utilizing an evolving Peephole LSTM (PLSTM). This sophisticated architecture incorporates peephole connections, allowing for enhanced information flow within the network. Subsequently, a hybrid evolutionary algorithm is introduced for fine-tuning the parameters of the peephole LSTM (PLSTM). This hybrid approach amalgamates the whale optimization algorithm (WOA) and cultural algorithm (CA), harnessing their complementary strengths for optimal parameter configuration. The study extends beyond model development to address the practical implications of real-time electric load forecasting. A comprehensive stability analysis is conducted on datasets sourced from an urban area, offering insights into the model's reliability and performance under dynamic conditions. This tripartite investigation not only contributes to the advancement of predictive modeling in electric load demand forecasting but also underscores the significance of robust parameter optimization and stability assessments for real-world applicability in urban energy management. The simulation results on the practical dataset clearly show the high robustness and capabilities of the proposed model. By combining cutting-edge model architecture, hybrid evolutionary optimization, and thorough stability assessments, this study not only contributes to the forefront of predictive modeling in electric load forecasting but also emphasizes the critical importance of parameter optimization and stability evaluations for the seamless integration of such models into the dynamic landscape of urban energy management.

关键词： Electric load forecasting Peephole LSTM Hybrid evolutionary algorithm whale optimization algorithm Cultural algorithm Energy law and policy analysis

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A novel hybrid optimized incremental relevance vector machine and filtering technique for state of charge estimation of lithium-ion batteries

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JOURNAL OF ENERGY STORAGE 2024年 90卷

作者： Wang, Chao Zhang, Xin Yun, Xiang Fan, Xingming Guilin Univ Elect Technol Dept Elect Engn & Automat Guilin 541004 Peoples R China

Despite not requiring a comprehensive understanding of the intricate electrochemical reactions within the battery, the data-driven approach faces challenges regarding state of charge (SOC) fluctuations during abrupt changes in battery current. Currently, research on data-driven filtering methods predominantly focuses on neural network-based algorithms, with limited exploration of the relevance vector machine (RVM) algorithm that possesses advantages in handling small sample sizes, high sparsity, and probabilistic distributions. Drawing inspiration from this premise, the present manuscript introduces an innovative fusion algorithm, founded upon the framework of the adaptive Kalman filtering (AKF) algorithm, which intricately melds the optimized incremental RVM (OIRVM) with the Coulomb counting methodology, called Coulomb_AKF_OIRVM (CFR) algorithm. Experimental findings suggest that the CFR algorithm displays exceptional generalization capacity across different driving cycles and temperatures, exhibiting high estimation accuracy and robustness. The average root mean square error, determination coefficient, mean absolute error and maximum absolute error of the public datasets and experimental datasets are 0.25 %, 0.02 %, 0.20 %, and 0.85 %, respectively.

关键词： Optimized incremental relevance vector machine whale optimization algorithm Adaptive Kalman filter SOC estimation Experimental test

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