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S-box generation algorithm based on hyperchaotic system and its application in image encryption

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第17期82卷 25559-25583页

作者： Yang, Sen Tong, Xiaojun Wang, Zhu Zhang, Miao Harbin Inst Technol Comp Sci & Technol Weihai 264200 Peoples R China

S-box is the only non-linear device in the cryptographic algorithm, and its quality determines the lower limit strength of the cryptographic algorithm. However, because the image data is highly correlated, the traditional encryption methods and their S-boxes, such as AES and DES are not suitable for using in image encryption. Based on this, this paper proposes an S-box generation algorithm based on a 4D hyperchaotic system and improved particle swarm optimization. Firstly, this paper improves on the Lorenz chaotic system and proposes a 4D hyperchaotic system with a higher Lyapunov exponent and more complex dynamics. Secondly, the idea of simulated annealing algorithm is introduced into the particle swarm optimization algorithm, which further improves the efficiency of the particle swarm optimization algorithm and improves the problem that the particle swarm optimization algorithm is easy to fall into the local optimal solution. Then an improved particle swarm optimization algorithm is used to optimize the nonlinearity of the S-box to improve the performance of the S-box. Finally, use the generated S-box to design an image encryption algorithm and prove the security of the S-box. The experimental results show that the S-box designed in this paper has excellent performance in the five indicators of Nonlinearity, SAC, BIC-NL, LP, and DP. At the same time, the encryption result can resist common attacks, so it has strong multimedia security.

关键词： 4D hyperchaotic system S-box Image encryption particle swarm optimization algorithm Simulated annealing algorithm

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Data adjusting strategy and optimized XGBoost algorithm for novel insider threat detection model

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2023年第16期360卷 11414-11443页

作者： Kan, Xiu Fan, Yixuan Zheng, Jinjie Chi, Chi-hung Song, Wanqing Kudreyko, Aleksey Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Natl Univ Singapore Sch Comp Singapore 119275 Singapore Ufa Univ Sci & Technol Dept Gen Phys Zaki Valedi 32 Ufa 450076 Russia

With the growth of access to internal system resources, the insider threat problem is emerging and can bring immeasurable losses to enterprises. In order to detect the hidden threats and guide the formulation of enterprise management strategy, it is important to analyze and understand the employee behavior. Thus, we suggest a user behavior analysis system framework, which mainly includes data processing, user behavior modeling and results analysis. Data Adjusting (DA) strategy and optimized eXtreme Gradient Boosting (XGBoost) model are utilized with the aim of full analysis small amount of feature information. The strategy for detecting suspicious behavior can be the following. Firstly, select initial suspicious data, misclassification data retention and combination sampling. Secondly, for further behavior model construction, an improved particle swarm optimization algorithm based on ethnic randomized particles (ERPSO), which introduces Gaussian white noise with adjustable intensity into acceleration coefficients is given for searching the optimal XGBoost parameters. In addition, based on the designed DA strategy and the proposed ERPSO algorithm, we have also compared the results of the proposed methods with the current state-of-the-art methods. Experimental results show that the XGBoost optimized by the ERPSO (ERPSO-XGBoost) model has comprehensive performance, which proves the rationality and effectiveness of the insider behavior analysis system framework. Through a comprehensive understanding of insider behavior, the obvious characteristic behavior is found to adjust the management strategy and guide the behavior modeling, so as to prevent more losses in time. (c) 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.

关键词： Insider threat Behavior modeling particle swarm optimization algorithm eXtreme Gradient Boosting Management strategy

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Control of thermal uniformity in microwave heating process by BPNN and adaptive particle swarm optimization

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Heliyon 2024年第21期10卷 e37971页

作者： Yao, Qing Zheng, Xin Wang, Rui Liang, Wenlong Liu, Tie Chu, Wenxiao Key Laboratory of Healthy & Intelligent Kitchen System Integration of Zhejiang Province Ningbo 315336 China Ningbo Fotile Kitchen Ware Company Zhejiang Ningbo 315336 China Key Laboratory of Thermal-Fluid Science and Engineering Ministry of Education Xi'an Jiaotong University Shaanxi Xi'an 710049 China

As China advances its green economy, innovative methods are being employed to enhance energy optimization and conservation within energy-intensive industries. Among these methods, microwave heating stands out due to its superior heating efficiency and lack of pollution. Nonetheless, uniform heating remains a challenge because the microwave absorption capacity of the heated medium varies with changes in heating time and temperature. To address this issue, an Adaptive particle swarm optimization (APSO) neural network microwave system based on the Back Propagation Neural Network (BPNN) is proposed. This system leverages the fundamental principles of particle swarm optimization (PSO), categorizing particle swarms into three types and iterating them through distinct processes to achieve optimal results. An APSO controller is designed based on system identification, adjusting the controller parameters according to the error between the real-time system output and the identification model. The feedback error is used as the fitness function in the PSO algorithm, continuously adjusting the weights and thresholds of the neural network. This intelligent control approach optimizes the microwave oven's input power to minimize the error between the actual temperature output and the identified temperature. The APSO controller is designed based on system identification, with the intelligently controlled microwave heating system adjusting its input power to minimize the error between the identified and actual temperature outputs. Unlike traditional Proportional Integral Differential (PID) and BPNN controllers, this approach calculates the output of the identified model and the error of the actual model, feeding this information back to the controller. The feedback error serves as the fitness function in the PSO algorithm, enabling continuous adjustment of the network's weights and thresholds to regulate the microwave equipment's output power, thereby ensuring the output temperature

关键词： APSO controller Back propagation neural network Control system Microwave heating particle swarm optimization algorithm

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Application of Genetic and particle swarm optimization algorithms to Portfolio optimization Problem: Borsa Istanbul and Crypto Money Exchange 4

Application of Genetic and Particle Swarm Optimization Algor...

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4th International Conference on Computer Science and Engineering (UBMK)

作者： Huseyinov, Ilham Ulucay, Samed Istanbul Aydin Univ Yazilim Muhendisligi Bolumu Istanbul Turkey Istanbul Aydin Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781728139647

particle optimization (PSO) algorithm and genetic (GA) algorithms are widely used in a variety of optimization problems. Portfolio optimization problems offer minimum risk and maximum profit-based solutions to help investors on optimal investment. Current researches have focused on PSO and GA algorithms based on Markowitz's mean variance method. This paper examines the implementation of PSO and GA algorithms to solve the optimization problem of two portfolios, Borsa Istanbul (BIST) and Cryptocurrency Exchange (KPB). This study also uses Markowitz mean variance method, that is, variance method for risk minimization and mean method for profit maximization. PSO and GA varying coefficients are compared in terms of sharpe risk percentage and portfolio profit. The comparison result is in favor of PSO algorithm.

关键词： Portfolio optimization Level Variance Model Genetic algorithm particle swarm optimization algorithm

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Dynamic scheduling of workshop resource in cloud manufacturing environment

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 138卷

作者： Hu, Yanjuan Pan, Leiting Pan, Xueqiao Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China 2055 Yan An St Changchun Jilin Peoples R China

In the cloud manufacturing environment, workshop resource scheduling serves as a pivotal component, characterized by increased dynamics and complexities. Nevertheless, existing dynamic scheduling methods are often limited to solving specific dynamic events. Thus, considering the actual workshop resource scheduling in a cloud manufacturing environment, this article examines the methods to address unexpected events including randomly arriving tasks, resource breakdown, as well as resource maintenance. Besides, a dynamic scheduling method based on the Game Theory, considering workshop capacity in cloud manufacturing, was developed. In the first place, the priority of workshop tasks was evaluated by Game Theory, and the optimal task processing sequence in the workshop was determined to maximize benefits. Secondly, to verify the dynamic regulation performance of the method, it was combined with the particle swarm optimization (PSO) algorithm considering multi-objective factors to obtain an ameliorated PSO algorithm addressing the challenge of resource optimization scheduling in a genuinely dynamic workshop environment. Finally, this method was tested through a case study, and the results demonstrate that it can achieve superior dynamic and static performance compared to alternative algorithms.

关键词： Workshop resource Dynamic scheduling Game theory particle swarm optimization algorithm Cloud manufacturing

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Economic optimization of business administration resources: Multi-objective scheduling method based on improved PSO

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Li, Sizheng Jiangxi Vocat Coll Mech & Elect Technol Sch Management Engn 168 Fenglin Ave Changbei Econ Dev Zone Nanchang 330000 Peoples R China

In the context of increasing environmental challenges and the demand for sustainable development, traditional resource scheduling models in business management often fail to balance economic efficiency with environmental constraints. To address this gap, this study proposes an enhanced particle swarm optimization (PSO) algorithm, termed OBLPSO, which integrates Opposition-Based Learning (OBL) and a perturbation mechanism. First, OBL generates a high-quality initial population to improve solution diversity, while a cosine curve adaptive strategy dynamically adjusts inertia weights to balance global exploration and local exploitation. Additionally, a perturbation mechanism expands the search range, preventing premature convergence. A multi-objective optimization model is established, incorporating task time, economic cost, and environmental impact (e.g., energy consumption and pollutant emissions) to maximize resource utilization and minimize ecological harm. Experimental results demonstrate that OBLPSO reduces task processing time by 29.7% and energy consumption by 16.1% compared to benchmark algorithms (e.g., ACO, GA, and standard PSO) under large-scale tasks (2000 tasks). The proposed method provides a robust solution for sustainable resource scheduling in an enterprise management environment with economic constraints.

关键词： Business management resource scheduling particle swarm optimization algorithm inverse learning perturbation mechanism resource mapping

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WPO: A Whale particle optimization algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 1-16页

作者： Huang, Ko-Wei Wu, Ze-Xue Jiang, Chang-Long Huang, Zih-Hao Lee, Shih-Hsiung Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Kaohsiung Taiwan Natl Kaohsiung Univ Sci & Technol Dept Intelligent Commerce Kaohsiung Taiwan

Metaheuristic algorithms are novel optimization algorithms often inspired by nature. In recent years, scholars have proposed various metaheuristic algorithms, such as the genetic algorithm (GA), artificial bee colony, particle swarm optimization (PSO), crow search algorithm, and whale optimization algorithm (WOA), to solve optimization problems. Among these, PSO is the most commonly used. However, different algorithms have different limitations. For example, PSO is prone to premature convergence and falls into a local optimum, whereas GA coding is difficult and uncertain. Therefore, an algorithm that can increase the computing power and particle diversity can address the limitations of existing algorithms. Therefore, this paper proposes a hybrid algorithm, called whale particle optimization (WPO), that combines the advantages of the WOA and PSO to increase particle diversity and can jump out of the local optimum. The performance of the WPO algorithm was evaluated using four optimization problems: function evaluation, image clustering, permutation flow shop scheduling, and data clustering. The test data were selected from real-life situations. The results demonstrate that the proposed algorithm competes well against existing algorithms.

关键词： Metaheuristic algorithm particle swarm optimization algorithm Whale optimization algorithm Whale particle optimization algorithm optimization problems

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Grid Scheduling Strategy Considering Electric Vehicles Participating in Multi-microgrid Interaction

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第3期18卷 1557-1572页

作者： Yu, Zexu Dou, Zhenhai Zhao, Ye Xie, Ruishuo Qiao, Mengmeng Wang, Yuanyuan Liu, Lianxin Shandong Univ Technol Coll Elect & Elect Engn Zibo 255049 Peoples R China

With the expansion of the scale of power grid and the increase of the number of microgrids, the energy interaction between microgrids using contact lines will greatly increase the computation of the superior dispatching center. Therefore, the contact lines between microgrids are cancelled, so that the microgrids can realize energy interaction through the distribution network. However, this method may lead to overload of distribution network connections while reducing the computation. Due to its good transfer characteristics, the addition of electric vehicles (EVs) can alleviate the contact line pressure and realize the load transfer of microgrids. On this basis, a grid dispatching model based on the participation of EVs in microgrid interaction is proposed. Contact lines between microgrids are replaced by EVs and 100% transmission through the distribution network. The grid load is optimized with the goal of minimizing total cost, maximizing renewable energy utilization, and maximizing profit of each integrator. In the process of model optimization, aiming at the problem that the speed factor in the particle swarm optimization algorithm cannot take into account the optimal direction and the optimal step size, the adaptive time factor is added to establish a two-layer improved particle swarm optimization algorithm, which realizes the cooperative optimization of load and electricity price. The simulation results show that the total cost and underutilization of renewable energy of IEEE33-node system are reduced by 13.79% and 67.85%, compared with the traditional interaction mode, while they are reduced by 0.425% and 6.11% in IEEE43-node system.

关键词： Electric vehicles Multi-microgrid Interaction particle swarm optimization algorithm Cost benefit analysist Optimal operation

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Evaluation of problems and countermeasures in marketing teaching based on cloud computing data aggregation algorithm

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SN APPLIED SCIENCES 2023年第5期5卷 1-12页

作者： Shen, Weinan Suzhou Vocat Univ Sch Management Suzhou 215000 Jiangsu Peoples R China

In this study, we present a highly practical marketing course. Through learning marketing, we can cultivate students' ability to analyze, solve and practice problems. Therefore, this paper would study the problems encountered in the marketing course through cloud computing technology and propose corresponding countermeasures, so as to better improve students' ability to analyze and solve problems and enhance the effectiveness of the classroom. This paper also analyzed the data aggregation algorithm based on particle swarm optimization (PSO) algorithm. The results showed that when other conditions were the same, only 6 students approve of the traditional marketing teaching mode, accounting for 12%;there were 44 people who did not approve, accounting for 88%. This number was more than half;49 students recognized the optimized marketing teaching mode, accounting for 98%;there was 1 person who did not approve, accounting for 2%. The proportion of people who did not approve after optimization was far less than that of the traditional model, which showed that cloud computing data aggregation algorithm could find out the problems in marketing teaching and provide correct countermeasures to solve them, so that the teaching model could be optimized. (1) The data aggregation algorithm based on cloud computing has a good optimization effect on the evaluation of marketing teaching problems and countermeasures, indicating a new direction for future research. (2) The combination of cloud computing and data aggregation algorithm is not only a novel research perspective, but also a perfect idea. (3) Marketing course is a very important course in today's society. The study of its teaching problems and countermeasures evaluation will help students improve their learning efficiency and enhance their ability to adapt to the society.

关键词： Marketing teaching Cloud computing Data aggregation algorithm Analysis of problems and countermeasures particle swarm optimization algorithm

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Economic evaluation of energy storage integrated with wind power

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CARBON NEUTRALITY 2023年第1期2卷 1-18页

作者： Zhang, Xinjing Feng, Lu Li, Xiaoyu Xu, Yujie Wang, Liang Chen, Haisheng Chinese Acad Sci Inst Engn Thermophys Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Energy storage can further reduce carbon emission when integrated into the renewable generation. The integrated system can produce additional revenue compared with wind-only generation. The challenge is how much the optimal capacity of energy storage system should be installed for a renewable generation. Electricity price arbitrage was considered as an effective way to generate benefits when connecting to wind generation and grid. This wind-storage coupled system can make benefits through a time-of-use (TOU) tariff. A proportion of electricity is stored from the wind power system at off-peak time (low price), and released to the customer at peak time (high price). Thus, extra benefits are added to the wind-storage system compared with wind-only system. A particle swarm optimization (PSO) algorithm based optimization model was constructed for this integrated system including constraints of state-of-charge (SOC), maximum storage and release powers etc. The proposed optimization model was to obtain the optimal capacity of energy storage system and its operation control strategy of the storage-release processes, to maximize the revenue of the coupled system considering the arbitrage. Furthermore, the energy storage can provide reserve ancillary services for the grid, which generates benefits. The benefits of energy storage system through reserve ancillary services were also calculated. A case study was analyzed with respect to yearly wind generation and electricity price profiles. The benefit compared with no energy storage scenario was calculated. The impact of the energy storage efficiency, cost and lifetime was considered. The sensitivity and optimization capacity under various conditions were calculated. An optimization capacity of energy storage system to a certain wind farm was presented, which was a significant value for the development of energy storage system to integrate into a wind farm.

关键词： Energy storage Wind-storage coupled system Electricity price arbitrage and ancillary services Compressed air energy storage particle swarm optimization algorithm

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