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Fast Search of Lightweight Block Cipher Primitives via swarm-like Metaheuristics for Cyber Security

ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2021年第4期21卷 1–15页

作者： Jin, Xin Duan, Yuwei Zhang, Ying Huang, Yating Li, Mengdong Mao, Ming Singh, Amit Kumar Li, Yujie Beijing Elect Sci & Technol Inst 7 Fufeng Rd Beijing Peoples R China State Key Lab Cryptol POB 5159 Beijing 100878 Peoples R China Natl Inst Technol Patna Patna Bihar India Kyushu Inst Technol Kitakyushu Fukuoka Japan

With the construction and improvement of 5G infrastructure, more devices choose to access the Internet to achieve some functions. People are paying more attention to information security in the use of network devices. This makes lightweight block ciphers become a hotspot. A lightweight block cipher with superior performance can ensure the security of information while reducing the consumption of device resources. Traditional optimization tools, such as brute force or random search, are often used to solve the design of Synunetric-Key primitives. The metaheuristic algorithm was first used to solve the design of Symmetric-Key primitives of SKINNY. The genetic algorithm and the simulated annealing algorithm are used to increase the number of active S-boxes in SKINNY, thus improving the security of SKINNY. Based on this, to improve search efficiency and optimize search results, we design a novel metaheuristic algorithm, named particle swarmlike normal optimization algorithm (PSNO) to design the Symmetric-Key primitives of SKINNY. With our algorithm, one or better algorithm components can be obtained more quickly. The results in the experiments show that our search results are better than those of the genetic algorithm and the simulated annealing algorithm. The search efficiency is significantly improved. The algorithm we proposed can be generalized to the design of Symmetric-Key primitives of other lightweight block ciphers with clear evaluation indicators, where the corresponding indicators can be used as the objective functions.

关键词： Lightweight block cipher SKINNY metaheuristic algorigthm particle swarm optimization algorithm

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Modeling and Performance optimization of Wireless Sensor Network Based on Markov Chain

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IEEE SENSORS JOURNAL 2021年第22期21卷 25043-25050页

作者： Liu, Yutang Zhang, Qin Henan Inst Technol Sch Sci Xinxiang 453002 Henan Peoples R China Xinxiang Univ Sch Math & Stat Xinxiang 453003 Henan Peoples R China

Wireless sensor networks are usually deployed in areas with relatively harsh natural environments, and the collection node transmits data to the destination node through a multi-hop route. Therefore, how to effectively plan the transmission path is an important issue. This paper combines the unbiased gray model with the Markov chain model to establish an unbiased gray Markov chain model, and points out that the unbiased gray Markov chain model also has shortcomings in parameter selection. The particle swarm algorithm is used to improve it, and the mathematical model, calculation principle and various parameters of the particle swarm optimization algorithm are introduced, and the implementation flow chart of the particle swarm algorithm is given. Aiming at the shortcomings of the unbiased gray Markov chain model, the particle swarm algorithm and the unbiased gray Markov chain model are combined to form the particle swarm unbiased gray Markov chain model. The simulation environment and the training environment of the particle swarm unbiased grey Markov chain model were designed in the experiment. The node scheduling optimization experiment proves that the scheduling method based on the particle swarm unbiased grey Markov chain model has achieved better results in coverage and energy consumption balance than the random and shortest distance method. In the routing experiment, the experimental analysis of the node's Q value proved the convergence of the algorithm, and compared with other protocols, it proved that the routing algorithm can effectively extend the network life cycle and achieve load balancing.

关键词： Sensors Wireless sensor networks Markov processes Mathematical model Routing particle swarm optimization Monitoring Wireless sensor network Markov chain particle swarm optimization algorithm routing protocol unbiased gray model

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A novel adaptive cascade controller design on a buck-boost DC-DC converter with a fractional-order PID voltage controller and a self-tuning regulator adaptive current controller

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IET POWER ELECTRONICS 2021年第11期14卷 1920-1935页

作者： Mollaee, Hasan Ghamari, Seyyed Morteza Saadat, Seyyed Amirhossein Wheeler, Patrick Shahroud Univ Control & Robot Fac Semnan *** Iran Univ Nottingham Dept Elect & Elect Engn Nottingham England

The design of a cascade controller is demonstrated for a buck-boost converter that is combined with two control loops consisting of inner and outer controllers. The outer loop is implemented by a fractional-order proportional-integrated-derivative (FO-PID) controller that works as a voltage controller and generates a reference current for the inner control loop. To provide faster dynamic performance for inner loop, a self-tuning regulator adaptive controller, which tries to regulates the current with the help of a novel improved exponential regressive least square identification in an online technique, is designed. Moreover, in the outer loop, to tune the gains of the FO-PID controller, a novel algorithm of antlion optimizer algorithm is used that offers many benefits in comparison with other algorithms. The system provided by the boost mode is a non-minimum phase system, which creates challenges for designing a stable controller. In addition, a single loop controller is proposed based on a PID controller tuned by a particle swarm optimization algorithm to be compared with the cascade controller. Cascade loop can present significant benefits to the controller such as better disturbance rejection. Finally, the strength of the presented cascade control scheme is verified in different performing situations by real-time experiments.

关键词： particle swarm optimisation exponential regressive least square identification antlion optimizer algorithm boost mode Control of electric power systems Optimisation techniques fractional-order PID voltage controller nonminimum phase system cascade control outer controllers self-tuning regulator adaptive current controller inner control loop regression analysis Control system analysis and synthesis methods cascade loop FO-PID controller Current control DC-DC power convertors stability particle swarm optimization algorithm fractional-order proportional-integrated-derivative controller least squares approximations electric current control adaptive cascade controller design outer loop stable controller Voltage control Stability in control theory single loop controller control system synthesis Interpolation and function approximation (numerical analysis) buck-boost DC-DC converter three-term control adaptive control Self-adjusting control systems voltage control

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An Efficient optimization Design for 1 MHz Ultrasonic Transmitting Transducer

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IEEE SENSORS JOURNAL 2021年第6期21卷 7420-7427页

作者： Chen, Dongdong Zhao, Jianxin Fei, Chunlong Li, Di Liu, Yusang Chen, Qiang Lou, Lifei Feng, Wei Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Columbia Univ Sch Engn & Appl Sci New York NY 11101 USA

Due to the low efficiency of traditional design method, an efficient optimization design method is developed to design and fabricate high-performance ultrasonic transmitting transducer (UTT) based on PiezoCAD software and particle swarm optimization (PSO) algorithm. Based on the data of design and performance parameters of UTT obtained by PiezoCAD software, the genetic algorithm-based back-propagation neural networks (GA-BPNNs) are established to describe their mapping relationship. The optimality criterions are established based on the performance parameters including center frequency (CF) and maximum echo amplitude (MEA). Then, based on the established GA-BPNNs and optimality criterions, the optimization design method of UTT is developed under the framework of PSO algorithm. According to the desired performance, the design parameters of UTT, including the thickness and diameter of PZT-4 (Pb(ZrxTi1-x)O-3), the acoustic impedance of backing, are optimized by the developed method, and they are 2 mm, 30 mm and 3 MRayl, respectively. Based on the optimized design parameters, the CF and MEA simulated by PiezoCAD are 1.05 MHz and -45.1 dB, which are in good agreement with the designed ones (1 MHz and -45 dB). In addition, the CF of the fabricated UTT is 1.07 MHz, and its insertion loss is -7.2 dB at 1.075MHz, which implies that the fabricated UTT has excellent performance. Therefore, the developed optimization design method is effective to fabricate high-performance UTT.

关键词： optimization Acoustics Design methodology Software Impedance Transducers Genetic algorithms Ultrasonic transmitting transducer back-propagation neural network optimization design particle swarm optimization algorithm

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Structures and stabilities of UPbn (n ≤ 18) clusters: A first-principles global optimization calculation

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MATERIALS TODAY COMMUNICATIONS 2023年 36卷

作者： Gao, Chun Guo, Guina Hu, Shuxian Liu, Haitao Zou, Wenli Zhang, Ping Yan, Jun Inst Appl Phys & Computat Math Lab Computat Phys Beijing 100088 Peoples R China Northwest Univ Inst Modern Phys Xian 710127 Shaanxi Peoples R China Univ Sci & Technol Beijing Sch Math & Phys Beijing 100083 Peoples R China Shaanxi Key Lab Theoret Phys Frontiers Xian 710127 Shaanxi Peoples R China Peking Univ Ctr Appl Phys & Technol Beijing 100871 Peoples R China

Endohedrally doped clusters have received much attention because they can act as superatoms and have great potential as the building blocks for cluster-assembled materials. We have carried out a comprehensive study based on the combination of the particle swarming optimization algorithm and the relativistic density functional theory, to obtain geometric structures and stabilities of lead (Pb) clusters doped with one uranium (U) atom. A gradual evolution pattern was observed with the increasing number of Pb atoms, from exohedrally doped structures to quasi-endohedral structures, and finally to endohedrally doped structures. At least 12 and 11 Pb atoms were necessary to encapsulate the U atom completely in the calculation without and with the spin-orbit coupling (SOC) effect respectively. The UPb16 cluster was represented as a highly stable endohedral cage structure with the U atom around its center. In addition, the crucial role of the SOC effect was emphasized. We hope that our findings will provide a fundamental understanding of actinide-doped lead clusters, which may be quite different from their light element analogues.

关键词： Intermetalloid cluster Endohedrally doped cluster particle swarm optimization algorithm Density functional theory Relativistic effect

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Intelligent optimization algorithm-based electricity pricing strategy for smart building clusters

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Wang, Hui Liao, Xu Ji, Xiu Gong, Shanggao Meng, Xiangping Wang, Jiarui Changchun Inst Technol Sch Elect Engn & Informat Technol Changchun Peoples R China Natl & Local Joint Engn Res Ctr Smart Distribut Ne Changchun Peoples R China State Grid JILIN Elect Power Res Inst Changchun Peoples R China

With the continuous infusion of renewable energy sources, smart buildings have evolved from single-load characteristics into dual characteristics with both electric energy production and consumption capability. Concurrently, the peak and off-peak periods of electricity consumption are influenced by climatic factors, which leads to complexity and deviation from the time-of-use tariffs set by electricity markets, which consequently result in a loss of revenue from grid-based electricity sales. Thus, adopting an innovative pricing mechanism to offset the revenue deficit in the grid assumes paramount significance. Built upon a dual-layer framework that employs intelligent optimization algorithms, this study proposes a pricing strategy for introducing the retail electricity provider into smart building clusters with peer-to-peer power sharing as the core. First, the independent operation model of intelligent buildings and electric energy sharing model without the participation of retail power suppliers are respectively established. Subsequently, with the aim to minimize alliance costs, a novel energy sharing pricing model involving retail electricity suppliers is developed, and a combination of particle swarm optimization and alternating direction multiplier methods is used for distributed solutions within a representative model. This approach yields optimal energy sharing transaction volumes and pricing while ensuring the confidentiality of each participating entity. Lastly, from the perspectives of the power grid, retail electricity suppliers, and multi-building smart alliances, this study conducts simulation analyses of key parameters that influence the bargaining effectiveness of retail electricity suppliers. These parameters encompass the upper limit of pricing, market supervision coefficient, and discount coefficient associated with the grid-based electricity sales to suppliers. Through these analyses, the study further validates the efficacy of the proposed strate

关键词： smart buildings peer-to-peer power sharing retail electricity provider market bargaining particle swarm optimization algorithm alternating direction multiplier method

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A novel grey multivariate model for forecasting landslide displacement

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 103卷

作者： Li, S. H. Zhu, L. Wu, Y. Lei, X. Q. Southwest Jiaotong Univ Dept Geol Engn Chengdu 610031 Sichuan Peoples R China Chinese Acad Sci Inst Mt Hazards & Environm Key Lab Mt Hazards & Surface Proc Chengdu 610041 Sichuan Peoples R China

Forecasting landslide displacement is an important issue in engineering geology. In this field, it is difficult to accurately forecast the displacement of the step point of a step-type landslide. In this study, a novel grey multivariate model is proposed for forecasting landslide displacement. The proposed model transforms the original sequence by using the Hausdorff derivative operator, determines the model parameters by using the particle swarm optimization algorithm, and uses the trapezoidal integral formula to calculate the predicted value. Two numerical examples show that the average absolute relative error and mean squared error of the proposed model are smaller than those of the recursive discrete multivariate grey model and the multivariable grey model with structure compatibility. The proposed model is used to forecast the displacement of the Bazimen landslide in the Three Gorges Reservoir area of China. The previous month's displacement, precipitation, and change in the reservoir water level are used as input variables. The results show that the performance of the proposed model is superior to that of the extreme learning machine model. This paper provides an effective method for forecasting displacement of the step point of a step-type landslide.

关键词： Landslide displacement Forecast Grey multivariate prediction model Hausdorff derivative particle swarm optimization algorithm

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A particle swarm optimization algorithm and its application in hydrodynamic equations

A particle swarm optimization algorithm and its application ...

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2nd International Conference on Machinery, Materials Science and Engineering Applications (MMSE 2012)

作者： Zhou, Jianhui Zhao, Shuzhong Yue, Lixi Lu, Yannan Si, Xinyi Hebei United Univ Coll Qianan Tangshan Peoples R China Hehai Univ Coll Water Conservancy & Hydropower Engn Nanjing Peoples R China

ISBN: (纸本)9783037854099

In fluid mechanics, how to solve multiple solutions in ordinary differential equations is always a concerned and difficult problem. A particle swarm optimization algorithm combining with the direct search method (DSPO) is proposed for solving the parameter estimation problems of the multiple solutions in fluid mechanics. This algorithm has improved greatly in precision and the success rate. In this paper, multiple solutions can be found through changing accuracy and search coverage and multi-iterations of computer. Parameter estimation problems of the multiple solutions of ordinary differential equations are calculated, and the result has great accuracy and this method is practical.

关键词： particle swarm optimization algorithm Direct search method Ordinary differential equations Parameter estimation

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Improved Spectral Clustering Clothing Image Segmentation algorithm Based on Sparrow Search algorithm

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Journal of Donghua University(English Edition) 2022年第4期39卷 340-344页

作者： HUANG Wenan QIAN Suqin College of Information Science and Technology Donghua UniversityShanghai 201620China Engineering Research Center of Digitized Textile&Fashion Technology Ministry of EducationDonghua UniversityShanghai 201620China

In the process of clothing image researching,how to segment the clothing quickly and accurately and retain the clothing style details as much as possible is the basis of subsequent image *** clustering clothing image segmentation algorithm is a common method in the process of clothing image ***,the traditional model requires high computing power and is easily affected by the initial center of *** often falls into local *** at the above two points,an improved spectral clustering clothing image segmentation algorithm is proposed in this *** Nystrom approximation strategy is introduced into the spectral mapping process to reduce the computational *** the clustering stage,this algorithm uses the global optimization advantage of the particle swarm optimization algorithm and selects the sparrow search algorithm to search the optimal initial clustering point,to effectively avoid the occurrence of local *** the end,the effectiveness of this algorithm is verified on clothing images in each environment.

关键词： clothing segmentation spectral clustering particle swarm optimization algorithm intelligent fashion design

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A multi-objective optimization for planning of networked microgrid using a game theory for peer-to-peer energy trading scheme

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IET GENERATION TRANSMISSION & DISTRIBUTION 2021年第24期15卷 3423-3434页

作者： Ali, Liaqat Muyeen, S. M. Bizhani, Hamed Ghosh, Arindam Curtin Univ Dept Elect & Comp Engn Perth WA Australia Qatar Univ Dept Elect Engn Doha Qatar Ferdowsi Univ Mashhad Sun Air Res Inst Mashhad Razavi Khorasan Iran

In this paper, a multi-objective optimization technique is proposed for the planning of a networked microgrid based on peer-to-grid (P2G) and peer-to-peer (P2P) energy trading schemes. Two different criteria's including annual profit and energy index of reliability are taken into consideration to form a multi-objective function. The networked microgrid consists of three individual microgrids containing their own combinations of generation resources, batteries and residential loads. All microgrids are connected together and also to the main grid to meet the energy exchange requirements of P2P energy trading. A cooperative game theory technique based on a particle swarm optimization algorithm is used to model the networked microgrid, and to find the suitable sizes of the players that simultaneously maximize the payoff values of both objective functions. Besides, a comparative analysis is carried out for both P2G and P2P energy trading schemes. The results show that the outcomes are maximum when both criteria are considered in the optimization and P2P energy trading is carried out. The sensitivity analysis is performed on the selected parameters and verified the right change 0.003% and 4.5% in discount rate and electricity prices, respectively.

关键词： game theory technique P2P energy trading particle swarm optimisation game theory sensitivity analysis residential loads Control of electric power systems Power system management, operation and economics Optimisation techniques multiobjective optimization technique distributed power generation individual microgrids peer-to-grid batteries objective functions Distribution networks optimisation pricing Distributed power generation networked microgrid power distribution control energy exchange requirements multiobjective function peer-to-peer energy trading scheme particle swarm optimization algorithm

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