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Electronic Software Data Security Control Mechanism Based on intelligent optimization Algorithm

Procedia Computer Science 2025年 259卷 598-605页

作者： Xinyu Guo Dekyi Metok State Grid Beijing Electric Power Co. LTD. Beijing 100000 China State Grid Xizang Electric Power Co. LTD. Lhasa 850000 Tibet China

As electronic software prevails, data security has become an issue of great significance. Data security controls based on traditional methods have limitations in terms of resource utilization. Whether there are better methods to improve the system resource utilization and data security of the system is a focus of our study. In this paper, we propose a PSO algorithm-based data security control mechanism for electronic software. PSO algorithm will be the research method selected for our study. The mechanism employs the PSO algorithm to exploit parallel search and global optimization, which has the superiority in terms of resource utilization, and systematically enhance the system performance by adaptively and dynamically adjusting the mechanism. Through our experiments and evaluations, we find that the PSO algorithm-based data security control mechanism is more effective in terms of resource utilization than the traditional mechanism.

关键词： Electronic Software Data Security Control Mechanisms intelligent optimization algorithms Particle Swarm optimization

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Design of Antenna Rapid optimization Platform Based on intelligent algorithms and Surrogate Models 12

Design of Antenna Rapid Optimization Platform Based on Intel...

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12th International Symposium on Antennas, Propagation and Electromagnetic Theory (ISAPE)

作者： Jin, Fan Dong, Jian Wang, Meng Wang, Shan Cent S Univ Sch Software Changsha Hunan Peoples R China Cent S Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781538673027

Optimizers in electromagnetic (EM) simulation software are facing challenges in optimization efficiency, supported optimization types and optimization results usability. Aiming to fill these gaps, a new antenna design exploration scheme is proposed, called Antenna Rapid optimization Platform (AROP). The scheme adopts ANSOFT HFSS and MATLAB co-simulation method and provides a friendly graphical interface based on the antenna optimization process. State-of-the-art intelligent optimization algorithms and surrogate model methods are embedded, addressing efficient multi-objective antenna optimization problems and generating Pareto optimal solution sets. Design results show that the surrogate models assisted by evolutionary algorithms built into AROP can largely improve optimization capacity for high-performance antenna synthesis and deal with complex antenna designs with high-dimensional parameter space.

关键词： antenna design intelligent optimization algorithms surrogate models optimization simulation platform

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A resource allocation algorithm based on hybrid spider wasp optimization for cognitive radio networks

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PHYSICAL COMMUNICATION 2025年 70卷

作者： Shang, Shuo Zhou, Mingyue Changchun Univ Technol Sch Comp Sci & Engn Changchun Peoples R China

Cognitive radio (CR) is an effective technology for addressing spectrum scarcity, which can improve the utilization of spectrum resources through intelligent sensing and dynamic parameter adjustment. Since traditional resource allocation algorithms are difficult to adapt to the dynamic characteristics of cognitive radio environment, more and more researchers are focusing on intelligent optimization algorithms. Our objective is to maximize the channel capacity of cognitive transmitters under interference constraint at primary receiver, total transmit power constraint and fairness constraint in underlay cognitive radio networks. To enhance the flexibility of the algorithm, we transform the original constrained optimization problem into an unconstrained penalty function form. Given that the proposed problem is non-convex, we present the Spider Wasp optimization (SWO) algorithm to solve this optimization problem. To better search the solution space and avoid getting trapped in local optima, a hybrid Spider Wasp optimization algorithm (HSWO) is proposed. This algorithm integrates genetic algorithm (GA) principles to help the SWO algorithm in achieving the global optimum. Additionally, three different dynamic response strategies were proposed to validate the adaptability and flexibility of the proposed algorithm in dynamic environments. Simulation results show that HSWO and SWO algorithm can obtain higher system capacity and higher flexibility compared with the particle swarm optimization (PSO).

关键词： Cognitive radio intelligent optimization algorithms Spider Wasp optimization algorithm Flexibility

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intelligent optimization: Literature review and state-of-the-art algorithms (1965-2022)

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 126卷

作者： Mohammadi, Ali Sheikholeslam, Farid Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran Esfarayen Univ Technol Dept Elect & Comp Engn Esfarayen *** Iran

Today, intelligent optimization has become a science that few researchers have not used in dealing with prob-lems in their field. Diversity and flexibility have made the use, efficiency, and usefulness of various nature -inspired optimization methods, such as evolutionary and meta-heuristic algorithms, more evident in such problems. This work first provides a comprehensive overview of all considerations governing various optimi-zation problems with detailed corresponding categories. Then, the most comprehensive review and recent methods (during 1965-2022) are presented in evolution-based, swarm-based, physics-based, human-based, and hybrid-based categories. More than 320 new algorithms have been reviewed. All specifications including au-thors, year, abbreviation, inspired source, controls, and their application are considered in this regard. Statistical analyzes of papers and publishers, annually and for 57 years, along with their ranking, are also examined in detail. Among the key achievements of the paper include: the most number of algorithms with 47.71% (156 methods) have been from the swarm category, and most of them were published in the five years of 2021 (72, 22.02%), 2020 (39, 11.93%), 2022 (31, 9.48%), 2019 (26, 7.95%), and 2016 (21, 6.42%) respectively;the top five rankings of publishers of reviewed algorithms/papers were also: "Proceedings of the Congress" (33, 10.09%), "Applied Soft Computing" (19, 5.81%), "Expert Systems with Applications" (18, 5.51%), "Knowledge -Based Systems" (12, 3.67%), "Engineering Applications of Artificial Intelligence" (12, 3.67%), "Advances in Engineering Software" (12, 3.67%), " Neural Computing and Applications " (12, 3.67%), and " Information Sciences " (11, 3.36%). The paper's data is available at: https://***/ali-ece.

关键词： intelligent optimization algorithms Soft computing Literature review State-of-the-art Statistics

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Machine Learning Model for Predicting the Height of the Water-Conducting Fracture Zone Considering the Influence of Key Stratum and Dip Mining Intensity

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WATER 2025年第2期17卷 234-234页

作者： Che, Yuhang Cui, Ximin Wang, Yuanjian Li, Peixian China Univ Min & Technol Beijing Sch Geosci & Surveying Engn Beijing 100083 Peoples R China

Predicting the height of the water-conducting fracture zone (WCFZ) is crucial for preventing water inrush and ensuring safe underground mining operations. In this study, we propose a novel model combining CatBoost, XGBoost, and AdaBoost with SSA, HHO, and LEA. Key stratum data (DK, TK) and dip mining intensity data were integrated into the existing parameters for WCFZ height prediction. The main influence angle tangent, derived from the probability integral method, replaces the hard rock ratio coefficient. A total of 104 field datasets with eight input parameters were used, with WCFZ height as the dependent variable. The model was validated using five-fold cross-validation and evaluated with root mean square error (RMSE), mean absolute error (MAE), R2, and mean relative error (MRE). The Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) was applied to rank the models. The CAT-HHO model demonstrated the best performance. Using this model, predictions of WCFZ height under varying dip mining intensities showed an approximately linear relationship. SHAP analysis identified mining thickness as the most influential factor. Removing key stratum data from models significantly reduced prediction accuracy. The results highlight the model's ability to improve WCFZ height prediction, offering insights for water inrush prevention in coal mining operations and providing guidance for applying machine learning to similar challenges.

关键词： water-conducting fracture zone machine learning key stratum mining intensity intelligent optimization algorithms

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Integrated real-time intelligent control for wastewater treatment plants: Data-driven modeling for enhanced prediction and regulatory strategies

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WATER RESEARCH 2025年 274卷 123099页

作者： Dai, Wei Pang, Ji-Wei Ding, Jie Wang, Jing-hui Xu, Chi Zhang, Lu-Yan Ren, Nan-Qi Yang, Shan-Shan Harbin Inst Technol Sch Environm State Key Lab Urban Water Resource & Environm Harbin 150090 Peoples R China Harbin Corner Sci & Technol Inc Harbin 150023 Peoples R China Urban Water Resources Dev & Northern Natl Engn Res Harbin 150090 Peoples R China Urban Water Resources Co Ltd Natl Engn Res Ctr Harbin Inst Technol Harbin 150090 Peoples R China Yancheng Inst Technol Sch Environm Sci & Engn Yancheng 224051 Peoples R China

Both mechanical models and machine learning-based models are widely utilized for real-time dynamic control;however, their implementation in the water sector often incurs significant data and computational costs. To address these challenges, this study introduces an innovative feature extraction method designed to enhance the cost-effectiveness of dynamic control in wastewater treatment plants. The proposed method extracts dynamic features from time-series data of key substrate variables to construct a data-driven model and develop real-time control strategies. The results indicate that the data-driven model accurately predicts the variation trends of ammonia nitrogen, total nitrogen, and biochemical oxygen demand, with correlation coefficients exceeding 0.8. Compared to the traditional activated sludge 2D model, the proposed approach significantly improves computational efficiency, reducing model parameter calibration time from 939.75 s to 87.52 s. Furthermore, the developed real-time control strategies reduce energy consumption by up to 24.3% while ensuring effluent quality meets discharge standards. The inclusion of a dynamic update mechanism, which refreshes model parameters every three hours, further enhances system adaptability and responsiveness. In conclusion, the proposed method minimizes reliance on complex water quality, sludge, and environmental datasets by directly extracting dynamic biochemical characteristics from key variables, providing a cost-effective solution for dynamic control in wastewater management.

关键词： Data-drive model Dynamic characteristics Real-time control Biological nitrogen removal intelligent optimization algorithms

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Large-scale medical examination scheduling technology based on intelligent optimization

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JOURNAL OF COMBINATORIAL optimization 2019年第1期37卷 385-404页

作者： Zhang, Jiawei Wang, Ling Xing, Lining Natl Univ Def Technol Coll Informat Syst & Management Changsha 410072 Hunan Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

In recent years, the scale of the health examination business has increased rapidly, and research on the combinatorial optimization of medical examinations has become more important. In this context, a special large-scale flexible open shop scheduling problem (FOSP) is introduced based on the idea of the multi-processor open shop scheduling problem. A mixed integer programming model is developed for the FOSP, which regards client satisfaction as the most important objective. As the FOSP is particularly complex, three different intelligent optimization algorithms are examined, namely a genetic algorithm, hybrid particle swarm optimization, and simulated annealing. According to the medical examination preferences of the clients, a group of large-scale test problems are created on the basis of benchmark instances of the flexible job shop problem, and these are used to evaluate the performance of each algorithm. The experimental results show that the genetic algorithm outperforms both simulated annealing and hybrid particle swarm optimization, especially in large-scale problems.

关键词： Combinatorial optimization Flexible open shop scheduling problem Multi-processor open shop scheduling problem Mixed integer programming model Large-scale intelligent optimization algorithms

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Long-term Water Quality Prediction based on intelligent optimization and Seasonal-trend Decomposition 20

Long-term Water Quality Prediction based on Intelligent Opti...

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IEEE 20th International Conference on Automation Science and Engineering (CASE)

作者： Wang, Ziqi Wu, Xiangxi Bi, Jing Yuan, Haitao Zhang, Jia Zhou, MengChu Beijing Univ Technol Fac Informat Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Southern Methodist Univ Dept Comp Sci Lyle Sch Engn Dallas TX 75205 USA New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

ISBN: (纸本)9798350358513;9798350358520

Nowadays, the applications of water quality prediction in the field of regional water environment management are increasing. It refers to predicting the elemental values of the water environment in the future based on past monitoring data, which is essential to realize the real-time evaluation of water quality and dynamic control of pollution sources. However, the water environment indicators are affected by various elements, which have a large volatility and non-linear characteristics. In addition, most of the existing water quality predictions focus on single-step predictive modeling of single elements of the water environment and lack multi-step predictive analysis of multifactor data of the water environment. In this paper, a novel longterm prediction model based on genetic simulated annealing-based particle swarm optimization (GSPSO) with seasonaltrend decomposition using LOESS (STL) is proposed and named GSPSO-STL-Autoformer (GS-Autoformer). It realizes the multi-factor and long-term prediction of water quality time series data. Firstly, the Autoformer's hyperparameters are optimized by the GSPSO to improve its convergence speed. Secondly, the multi-factor features are decomposed by the STL to make the model more focused on learning feature information of each component. Finally, the long-term prediction is realized by the Autoformer. Comparative experiments with state-of-the-art peers show that the GS-Autoformer can effectively improve the accuracy of multi-factor and long-term predictions.

关键词： Time series forecasting seasonal-trend decomposition intelligent optimization algorithms

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Extraction of Single Diode Model Parameters of Solar Cells and PV Modules by Combining an intelligent optimization Algorithm with Simplified Explicit Equation Based on Lambert W Function

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ENERGIES 2023年第14期16卷 5425-5425页

作者： Li, Jianing Qin, Cheng Yang, Chen Ai, Bin Zhou, Yecheng Sun Yat Sen Univ Sch Mat Sci & Engn Guangzhou 510006 Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Photovolta Technol Guangzhou 510006 Peoples R China

In this paper, the explicit equation of the single diode model (SDM) expressed by the Lambert W function was reduced to its simplified form through variable replacement;then the simplified explicit equation was combined with an intelligent optimization algorithm to estimate the SDM parameters of solar cells and PV modules. To evaluate the parameter extraction performance of the new method, eight typical intelligent optimization algorithms were combined with the implicit, explicit, and simplified explicit equation to extract the SDM parameters of a solar cell and three types of PV modules. The results show that the new method not only improves the accuracy of parameter extraction but also enhances the robustness and convergence speed. Most importantly, the new method can nearly improve the parameter extraction accuracy of a poor-performing algorithm in traditional methods to the level of other well-performing algorithms without enhancing the algorithm itself. In a word, this study offers a new choice for a more accurate and reliable extraction of SDM parameters from both solar cells and PV modules.

关键词： single diode model parameter estimation solar cells photovoltaic modules Lambert W function intelligent optimization algorithms

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Distributed onshore wind farm siting using intelligent optimization algorithm based on spatial and temporal variability of wind energy

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ENERGY 2022年 258卷

作者： Gao, Yang Ma, Shaoxiu Wang, Tao Miao, Changhong Yang, Fan Henan Univ Key Res Inst Yellow River Civilizat & Sustainable Kaifeng 475001 Peoples R China Henan Univ Collaborat Innovat Ctr Yellow River Civilizat Hena Kaifeng 475001 Peoples R China Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Key Lab Desert & Desertificat Lanzhou 730000 Peoples R China

Distributed joint complementary wind generation is a powerful way to reduce the variability and improve the penetration of wind power. But distributed wind farm siting is a complex problem of nonlinear and global combinatorial optimization. Various correlation coefficients were used to measure the complementarity of renewable energy, which were not intuitive and accurate in former studies, and traditional siting methods are based on the abundance of wind energy, siting results cannot fully utilize the complementary potential of distributed wind energy. In this study, intelligent optimization algorithm is adopted to select the location of distributed wind farm in China mainland from the perspective of utilizing the complementarity of wind energy. The result showed that the existing wind power bases have obvious complementary superiority, combined power generation can improve availability from about 57.61% to 66.93%, reduce the long/short term variability confidence by about 40%. Siting result indicated that central and eastern Inner Mongolia, Xinjiang and Hexi Corridor are still key regions for the future wind farms construction. This study provides more direct evidence of the complementarity of existing wind power bases, and the proposed siting methodology can be applied in different regions worldwide.

关键词： Onshore wind energy Siting Spatial and temporal variability intelligent optimization algorithms

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