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Improved kepler optimization algorithm for enhanced feature selection in liver disease classification

KNOWLEDGE-BASED SYSTEMS 2024年 297卷

作者： Houssein, Essam H. Abdalkarim, Nada Samee, Nagwan Abdel Alabdulhafith, Maali Mohamed, Ebtsam Minia Univ Fac Comp & Informat El Minia Egypt Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia

Liver diseases represent a significant healthcare challenge, impacting millions globally and posing complexities in diagnosis. To address this global health concern, this paper introduces a groundbreaking enhancement to the kepler optimization algorithm, termed I-KOA, designed specifically for feature selection in high -dimensional datasets. By harnessing the synergies of Opposition -Based Learning and a Local Escaping Operator grounded in the k -nearest Neighbor (kNN) classifier, I-KOA asserts itself as a potent tool for local exploitation, balanced exploration, and evasion of local optima. To our knowledge, this is the first work to exploit KOA as a feature selection method. Pioneering the utilization of KOA as a feature selection method, the paper rigorously tests I-KOA in two extensive experiments, tackling the complex CEC'22 benchmark suite functions and the intricate landscape of five liver disease datasets. Results underscore I-KOA's unparalleled performance, validated through the Friedman test, where it surpasses seven rival optimization algorithms. Achieving an outstanding overall classification accuracy of 93.46%, Feature selection size of 0.1042, sensitivity of 97.46%, precision of 94.37%, and F1 -score of 90.35% across the liver disease datasets, I-KOA's randomized algorithm ensures robust feature selection, striking a compelling balance between subset size and classification efficacy. Acknowledging computational demands and generalization nuances, I-KOA is a formidable tool ready to revolutionize medical diagnosis and decision support systems.

关键词： Liver diseases Classification Machine learning kepler optimization algorithm Local Escaping Operator Opposition-based learning

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A Fractional Order-kepler optimization algorithm (FO-KOA) for single and double-diode parameters PV cell extraction

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HELIYON 2024年第16期10卷 e35771页

作者： Hakmi, Sultan Hassan Alnami, Hashim Ginidi, Ahmed Shaheen, Abdullah Alghamdi, Thamer A. H. Jazan Univ Coll Engn & Comp Sci Dept Elect & Elect Engn POB 114 Jazan 45142 Saudi Arabia Suez Univ Fac Engn Dept Elect Engn POB 43221 Suez Egypt Cardiff Univ Wolfson Ctr Magnet Sch Engn Cardiff CF24 3AA Wales Al Baha Univ Sch Engn Elect Engn Dept Al Baha 65779 Saudi Arabia

The primary objective of this study is to investigate the effects of the Fractional Order kepler optimization algorithm (FO-KOA) on photovoltaic (PV) module feature identification in solar systems. Leveraging the strengths of the original KOA, FO-KOA introduces fractional order elements and a Local Escaping Approach (LEA) to enhance search efficiency and prevent premature convergence. The FO element provides effective information and past expertise sharing amongst the participants to avoid premature converging. Additionally, LEA is incorporated to boost the search procedure by evading local optimization. The single-diode-model (SDM) and Doublediode-model (DDM) are two different equivalent circuits that are used for obtaining the unidentified parameters of the PV. Applied to KC-200, Ultra-Power-85, and SP-70 PV modules, FOKOA is compared to the original KOA technique and contemporary algorithms. Simulation results demonstrate FO-KOA's remarkable average improvement rates, showcasing its significant advantages and robustness over earlier reported methods. The proposed FO-KOA demonstrates exceptional performance, outperforming existing algorithms by 94.42 %-99.73 % in optimizing PV cell parameter extraction, particularly for the KC200GT module, showcasing consistent superiority and robustness. Also, the proposed FO-KOA is validated of on SDM and DDM for the well-known RTC France PV cell.

关键词： kepler optimization algorithm Single-diode model Double-diode model Parameters PV cell extraction

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A kepler optimization algorithm-Based Convolutional Neural Network Model for Risk Management of Internet Enterprises

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第7期15卷 210-210页

作者： Liu, Bin Zhou, Fengjiao Jiang, Haitong Ma, Rui Chengdu Univ Tradit Chinese Med Sch Management Chengdu Peoples R China Univ Sains Malaysia Sch Social Sci Gelugor Pinang Malaysia Sichuan Vocat Coll Culture & Commun Sch Cultural Management Chengdu 611230 Peoples R China Chengdu Univ Tradit Chinese Med Hlth & Rehabil Coll Chengdu Peoples R China

Internet enterprises, as the representative enterprises of technology-based enterprises, contribute more and more to the growth of the world economy. To ensure the sustainable development of enterprises, it is necessary to predict the risks in the operation of Internet enterprises. An accurate risk prediction model can not only safeguard the interests of enterprises but also provide certain references for investors. Therefore, this study designed a Convolutional Neural Network (CNN) model based on the kepler optimization algorithm (KOA) for risk prediction of Internet enterprises, aiming to maximize the accuracy of the prediction model, and to help Internet enterprises carry out risk management. Firstly, we select the indicators related to the financial risk of Internet enterprises, and predict the risk based on the traditional statistical analysis of Logistic regression model. On this basis, KOA was improved based on evolutionary strategies and fish foraging strategies, and the improved algorithm was applied to optimize CNN. Based on improved KOA and CNN algorithms, an IKOA-CNN risk prediction model is proposed. Finally, by comparing traditional statistical analysis-based models and other learning-based models, the results show that the IKOA-CNN algorithm proposed in this study has the highest prediction accuracy.

关键词： Risk management kepler optimization algorithm Convolutional Neural Network Internet enterprises

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An Improved Hybrid CNN-LSTM-Attention Model with kepler optimization algorithm for Wind Speed Prediction

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ENGINEERING LETTERS 2024年第10期32卷 1957-1965页

作者： Huang, Yuesheng Li, Jiawen Li, Yushan Lin, Routing Wu, Jingru Wang, Leijun Chen, Rongjun Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou 510665 Peoples R China

The flexibility and intermittency of wind speed is a crucial factor for its prediction. Research in this domain necessitates integrating diverse data to enhance model precision and reliability to meet the growing demand for wind energy in both the energy sector and the global economy. To this end, this work adopts the kepler optimization algorithm (KOA), which utilizes the position of each planet as a candidate solution. The optimization process then involves random updates to these candidate solutions, aiming to enhance wind speed prediction using the CNN-LSTM-Attention model. Specifically, it enhances the interpretability of the KOA for the global optimization problem. By using 1800 time moments of wind speed data with multidimensional features, the proposed hybrid model with KOA has been shown to outperform other optimization methods, demonstrating impressive performances in terms of mean absolute percentage error (MAPE), mean absolute error (MAE), runtime, root mean square error (RMSE), and R2. 2 . Hence, these results indicate its potential for improving wind speed prediction, which could open a novel direction in this field.

关键词： Deep learning kepler optimization algorithm CNN-LSTM-Attention Hybrid model Wind speed prediction

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An improved kepler optimization algorithm for module parameter identification supporting PV power estimation

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Heliyon 2024年第21期10卷 e39902页

作者： Moustafa, Ghareeb Alnami, Hashim Ginidi, Ahmed R. Shaheen, Abdullah M. Department of Electrical and Electronic Engineering College of Engineering and Computer Science Jazan University P.O. Box114 Jazan 45142 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Suez University P.O. Box: 43221 Suez Egypt

Identification of photovoltaic (PV) module characteristics in solar systems is a vital task, nowadays, for optimal PV power estimation. In this paper, this challenge task has been studied using a novel advanced kepler optimization algorithm (KOA). The standard version of KOA is adopted and assessed for getting the nine parameters of the PV triple diode model (3DM) considering three different practical PV modules. kepler's principles of planetary motion are used by KOA to forecast the location and velocity of planets at any particular moment. However, the success rate of the KOA is not compatible, and its efficiency needs to be enhanced. As a result, an Improved KOA (IKOA) is created by incorporating an advanced mechanism of Local Escaping Operator (LEO), resulting in improved process of searching with evading local optima. This mechanism means that the exploitation approach will activate with around half of the solutions for every iteration starting at the initial phase of the iteration journey. The suggested IKOA besides the standard KOA are developed for predicting PV parameters for three distinct PV modules which are Photowatt PWP201, R.T.C France and STM6-40/36. The results corresponding to the latest algorithms are also compared with the proposed IKOA about different published works. The simulation findings reveal that the suggested IKOA exhibits notable average improvement rates for the three modules of 62.27 %, 55.1 %, and 32.12 %, respectively. Furthermore, the suggested IKOA asserts significant superiority and robustness over previously reported results. © 2024

关键词： kepler optimization algorithm Local Escaping Operator Practical solar modules PV parameters extraction

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Energy management for microgrids integrating renewable sources and hybrid electric vehicles

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CASE STUDIES IN THERMAL ENGINEERING 2025年 69卷

作者： Liu, Wanying Rui, Chunqing Liu, Zilin Chen, Jinxin Fac Changan Univ Xian 710018 Shanxi Peoples R China Changan Univ Xian 710018 Shanxi Peoples R China Zhejiang Normal Univ Coll Engn 688 Yingbin Rd Jinhua 321004 Zhejiang Peoples R China

The incorporation of plug-in hybrid electric vehicles (PHEVs) offers a promising solution to tackle the energy crisis while reducing environmental challenges. With enhanced control and storage features, PHEVs contribute to greater flexibility in distribution networks. However, managing these vehicles alongside renewable energy sources (RES) presents significant challenges. This study proposes a novel Energy Management Strategy (EMS) for microgrids (MGs) integrating RES and PHEVs. The MG includes wind turbines (WT), photovoltaic panels (PV), micro turbines (MT), fuel cells (FC), storage batteries, PHEVs, and the grid. The primary objective is to minimize operational costs and environmental emissions, framed as an optimization problem. The EMS considers two RES operation scenarios and three electric vehicle (EV) charging mod- es-uncoordinated, coordinated, and smart-while addressing uncertainties in renewable energy generation and load demand considering the demand response program (DRP). It also in- corporates demand response mechanisms for greater resilience. The kepler optimization Algo- rithm (KOA), inspired by kepler's laws of planetary motion, is employed to tackle the nonlinear optimization problem. KOA models candidate solutions as planetary positions, continuously updating them relative to the optimal solution for comprehensive domain exploration. Simulation results reveal substantial improvements in cost and emission reductions. Without PHEVs, KOA achieves a mean operating cost of 109.623 ct, 1.46 % lower than benchmark methods, with minimal variability (standard deviation: 0.0002 ct). For maximum RES generation, costs are further reduced to 86.2143 ct with consistent performance (standard deviation: 0.0001 ct). When PHEVs are included, KOA demonstrates cost reductions of up to 17.9 % across various charging modes, showcasing its adaptability and efficiency in dynamic energy systems.

关键词： Demand response Energy management strategy kepler optimization algorithm Microgrid Renewable energy sources

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A Novel Monthly Runoff Prediction Model Based on KVMD and KTCN-LSTM-SA

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WATER 2025年第3期17卷 460-460页

作者： Zhang, Shujian Zhu, Kui Wang, Chaohe Xinjiang Univ Sch Geol & Min Engn Urumqi 830047 Peoples R China China Univ Min & Technol Sch Resources & Earth Sci Xuzhou 221116 Peoples R China China Univ Petr East China Sch Geosci Qingdao 266580 Peoples R China

Due to the uncertainty of meteorological factors and the influence of human activities, the monthly runoff series often exhibit the characteristics of non-stationarity. The appropriate prediction model and the hyperparameters of the model are often difficult to determine, and this affects the model prediction performance. For obtaining the accurate runoff prediction results, a novel prediction model (KVMD-KTCN-LSTM-SA) is proposed. This hybrid model uses kepler optimization algorithm (KOA)-optimized Variable Mode Decomposition (KVMD), KOA-optimized temporal convolutional network-long short-term memory (TCN-LSTM), and the self-attention (SA) mechanism. KVMD effectively reduces the difficulty of predicting the monthly runoff series, KOA helps to find the optimal hyperparameters of the model, TCN is combined with LSTM, and the SA mechanism effectively increases the performance of the model. Monthly runoff from three hydrological stations in the Hetian River basin and one hydrological station in the Huaihe River basin are predicted with the proposed model, and six models are selected for comparison. The KVMD-KTCN-LSTM-SA model effectively reduces runoff fluctuation and combines the advantages of multiple models and achieves satisfactory runoff prediction results. During the testing period, the proposed model achieves NSE of 0.978 and R2 of 0.982 at Wuluwati station, NSE of 0.975 and R2 of 0.986 at Tongguziluoke station, and NSE of 0.978 and R2 of 0.982 at Jiangjiaji station. The proposed hybrid model provides a new approach for monthly runoff prediction, which is capable of better managing and predicting mid-long-term runoff.

关键词： kepler optimization algorithm monthly runoff prediction self-attention temporal convolutional network-long short-term memory variational mode decomposition

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Design and optimization of Antipodal Vivaldi Antenna for Enhanced Breast Tumor Detection Using Microwave Imaging

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NATIONAL ACADEMY SCIENCE LETTERS-INDIA 2025年 1-6页

作者： Babu, Bindu Suresh, Sudha Easwari Engn Coll Dept Elect & Commun Engn Chennai 600089 Tamilnadu India

This paper presents a novel breast tumor identification strategy utilizing microwave imaging technology with specially designed antennas. The aim is to address the complexities arising from heterogeneity of breast tissues, which pose challenges for conventional breast cancer detection methods. To overcome these challenges, an Antipodal Vivaldi antenna (AVA) is developed, featuring dimensions of 53.07 x 46.07 x 0.6 mm(3). The design parameters of AVA are optimized using mutation boosted kepler optimization algorithm (MBKOA). Specific Absorption Rate (SAR) analysis on phantom breast with tumors is carried out utilizing finite-difference time-domain (FDTD) approach. To enhance signal strength at desired imaging location, Microwave Imaging beamforming algorithm is applied. Fast Delay Multiply and Sum (DMS) based image reconstruction algorithm is employed on time-domain data, providing high spatial response and comprehensive representation of internal structure of the breast.

关键词： Antipodal Vivaldi Antenna Mutation kepler optimization algorithm Heterogeneous Breast Phantom Breast Tumor Detection

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Predicting Rock Fracture Toughness Using a KOA-BP Neural Network: A Case Study from Five Provinces in China

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2024年第8期42卷 7963-7977页

作者： Wang, Zehang Lei, Yu Niu, Shuaishuai Luo, Xuedong Yu, Bingzhen Zhou, Zikang China Univ Geosci Wuhan Fac Engn 388 Lumo Rd Wuhan 430074 Hubei Peoples R China

Rock fracture toughness (RFT) is one of the most critical indicators in rock mechanics and is used to determine how fractures propagate in processes such as hydraulic fracturing, rock blasting, tunnel excavation, geothermal energy extraction, and CO2 sequestration. Determining this fracture toughness on the basis of various rock parameters is an ongoing research area. Previous studies have established simple regression relationships between fracture toughness and individual parameters, achieving varying degrees of success. However, these models are often based on specific rocks and locations, with input parameters selected through intuitive judgment rather than quantitative analysis, leading to a lack of broad acceptance. In this study, a kepler optimization algorithm (KOA)-optimized BP algorithm was utilized alongside four unoptimized machine learning algorithms and one empirical formula to predict fracture toughness from a set of common geomechanical parameters. Unlike many previous studies, the normalized mutual information (NMI) method was employed in this study to analyze the sensitivity of these parameters to RFT and assess their importance to the model. Quantitative analysis identified 8 parameters-R, B, S, alpha, T, UCS, Cc, and nu-out of the fifteen potential input parameters as having the highest correlation with fracture toughness, addressing the previous issue of parameter selection on the basis of intuition rather than statistical analysis. This study developed a KOA-BP neural network model as a powerful tool for predicting rock fracture toughness, integrating 3 essential modules. The data processing module normalizes and partitions the dataset to ensure consistency. The KOA optimization module employs the kepler optimization algorithm to iteratively optimize the weights and biases of the BP neural network, addressing challenges such as premature convergence and parameter sensitivity. Lastly, the BP neural network module performs forward propagation and

关键词： Rock fracture toughness Geotechnical engineering kepler optimization algorithm Predictive modeling Nonlinear data analysis

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Optimal power dispatch in hybrid power system for medium- and large-scale practical power systems using self-adaptive bonobo optimizer algorithm

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WIND ENGINEERING 2024年第6期48卷 1118-1140页

作者： Kouadri, Ramzi Mouassa, Souhil Jurado, Francisco Univ Setif 1 Elect Engn Dept Campus Maabouda Setif 19000 Algeria Univ Bouira Elect Engn Dept Bouira Algeria Univ Jaen Elect Engn Dept Jaen Spain

Incorporating renewable energy sources (RESs) introduces a notable amount of uncertainty in the optimal planning and operation of electrical power grids. Under these circumstances, this paper proposes the application of a recently introduced metaheuristic optimization technique to solve the stochastic optimal power flow (OPF) problem involving wind and solar power sources. The self-adaptive bonobo optimizer (SaBO) is used to minimize three distinct objective functions: (i) Total generation cost (TGC) minimization, including both thermal and wind/solar generation costs, (ii) Power loss minimization, (iii) Combined generation cost and emissions effect minimization. The costs associated with the stochastic generation of wind and solar power included direct costs, reserves and penalty costs from the overestimation and underestimation of available wind and solar power, respectively. The performance of the proposed algorithm is evaluated on two power systems: the modified IEEE 30-bus and the Algerian DZA 114-bus test systems. To demonstrate the efficacy of the SaBO, the obtained results have been compared with those obtained from the kepler optimization algorithm (KOA) and other recently published optimizers under the same case studies and constraints. The comparative results clearly show the superiority of the SaBO algorithm over all other well-known optimization algorithms provided in the literature for solving the OPF problem. This is evidenced by minimizing total generation costs of 781.2363 $/h for the modified IEEE 30-bus and 16,706.1630 $/h for the Algerian DZA-114-bus system. Furthermore, the integration of RES led to a notable 2.33% and 11.67% reduction in total generation cost for the IEEE 30-bus and Algerian DZA 114-bus systems, respectively, compared to their initial configurations without RESs. The promising findings highlight the powerful of the optimizer to solve non-linear and complex optimization problems in power systems.

关键词： Metaheuristic optimization technique optimal power flow self-adaptive bonobo optimizer renewable energy sources wind and solar generation combined generation cost and emissions kepler optimization algorithm

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