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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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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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Design of Intelligent Monitoring System for Blast Furnace Hearth Erosion 41

Design of Intelligent Monitoring System for Blast Furnace He...

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第41届中国控制会议

作者： Shuqiao Wang Yan Yuan Jingjie Liang Zhuofu Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper takes the iron-making blast furnace in operation of a steel plant as the specific object,analyzes the erosion status and characteristics of different parts of the furnace hearth and bottom combined with iron-making process and erosion mechanism of the blast furnace,designs an intelligent monitoring system for erosion of the furnace hearth lining,visualizes the temperature measurement *** erosion calculation model is established by using the heat transfer mechanism to quickly solve the erosion boundary for the area with low erosion degree and rate,and performs a fast solution of the erosion *** areas with high erosion degree and rate,the particle swarm optimization algorithm is selected by using its adaptive *** solves the erosion boundary accurately from the rough solution of the mechanism model,and the residual thickness of furnace lining is output and displayed,which helps ironmakers optimize the blast furnace production operation,maintenance strategy,improve production efficiency and reduce energy consumption.

关键词： Blast furnace lining erosion particle swarm optimization algorithm Intelligent monitoring system

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Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO

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ENERGIES 2021年第20期14卷 6514-6514页

作者： Yi, Min Xie, Wei Mo, Li Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China

In the electricity market environment, the market clearing price has strong volatility, periodicity and randomness, which makes it more difficult to select the input features of artificial neural network forecasting. Although the traditional back propagation (BP) neural network has been applied early in electricity price forecasting, it has the problem of low forecasting accuracy. For this reason, this paper uses the maximum information coefficient and Pearson correlation analysis to determine the main factors affecting electricity price fluctuation as the input factors of the forecasting model. The improved particle swarm optimization algorithm, called simulated annealing particle swarm optimization (SAPSO), is used to optimize the BP neural network to establish the SAPSO-BP short-term electricity price forecasting model and the actual sample data are used to simulate and calculate. The results show that the SAPSO-BP price forecasting model has a high degree of fit and the average relative error and mean square error of the forecasting model are lower than those of the BP network model and PSO-BP model, as well as better than the PSO-BP model in terms of convergence speed and accuracy, which provides an effective method for improving the accuracy of short-term electricity price forecasting.

关键词： electricity price forecast maximum information number Pearson coefficient BP neural network particle swarm optimization algorithm simulated annealing algorithm

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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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Equipment maintenance task prediction model analysis based on BAS-PSO hybrid optimization algorithm

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2021年第1期9卷 455-466页

作者： Song, Weixing Wu, Jingjing Chang, Huiqiang Xu, Guihua Army Engn Univ PLA Shijiazhuang Hebei Peoples R China Western Theater Army Dept Logist Lanzhou Peoples R China Unite 31682 PLA Lanzhou Peoples R China Unite 32272 PLA Lanzhou Peoples R China

Aiming at the maintenance task prediction problem of armored forces, the macro model and micro model are established to analyze the constraint conditions, and the equipment maintenance task prediction model is established in order to meet the motor hours echelon storage. Under the condition of meeting the balance of annual motor hours payments, the motor hours consumed by equipment are allocated according to the annual training tasks, and a hybrid optimization algorithm of improved particle swarm optimization is designed to solve the model, and a case study is carried out on a few vehicles in a certain army. The simulation results show that the model can effectively solve the problem of equipment maintenance task prediction, and a provide reference value for troops to make the maintenance plans.

关键词： Maintenance tasks motor hours balance of payments echelon storage equipment utilization beetle antennae search algorithm particle swarm optimization algorithm

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Design and key technology of the energy consumption management system for the liquid cooling data center

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ENERGY SCIENCE & ENGINEERING 2023年第3期11卷 1284-1293页

作者： Lai, Yibo Fang, Xiang Han, Rongjie Xuan, Yi Huang, Jiabin Hangzhou Power Supply Co State Grid Zhejiang Elect Power Co Ltd Hangzhou Peoples R China Hangzhou Power Supply Co State Grid Zhejiang Elect Power Co Ltd Hangzhou 310009 Peoples R China

In view of the serious problem of energy consumption waste in the application process of liquid cooling data center, a new energy consumption management system of liquid cooling data center is constructed in this research. Energy consumption predictor, resource controller and resource configurator are used to monitor and manage energy consumption and optimize resource allocation of liquid cooling data center. The S3C2440A microprocessor with the internal core of ARM920T is adopted as the core controller of the energy consumption data collection system, and the energy consumption sampling circuit is designed with the voltage transformer and the current transformer, and attenuation network is adopted to prevent frequency aliasing in data sampling. particle swarm optimization algorithm is used to identify the parameters in the model estimator, and the resource coordinator is used to solve the power consumption and the performance models. When using multiple physical servers to simulate the data center environment, the experimental structure shows that the system in the research can control the overall energy consumption of the server within 260 W, and the prediction error of the model estimator is kept lower than 2.4%.

关键词： energy consumption management system energy consumption sampling circuit liquid cooling data center particle swarm optimization algorithm resource configurator

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algorithms for solving assembly sequence planning problems

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NEURAL COMPUTING & APPLICATIONS 2021年第2期33卷 525-534页

作者： Su, Yingying Mao, Haixu Tang, Xianzhao Shenyang Univ Sch Mech Engn Shenyang 110044 Liaoning Peoples R China

Assembly sequence planning is one of the key issues in DFA and computer-aided assembly process planning research for concurrent engineering. The purpose of this paper is to solve the problem of insufficient individual intelligence in evolutionary algorithms for assembly sequence planning, and a evolutionary algorithm for assembly sequence planning is designed. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the hybrid assembly sequence planning and assembly line balance problems. According to the assembly sequence problem, the number of assembly tool changes and the number of assembly orientation changes are transformed into the operation time of the assembly line. At the same time, the transportation of heavy parts in the assembly balance problem is considered. Then, by extracting the connection relationship and information of the parts, the disassembly method is used to inversely obtain the disassembly support matrix, and then, it is used to obtain the priority relationship diagram of the assembly operation tasks that indicate the order constraints of the job tasks on the assembly line. Aiming at the shortcoming that particle swarm optimization algorithm is easy to fall into local optimum, a various population strategy is adopted to shorten the evolution stagnation time, improve the evolution efficiency of particle swarm optimization algorithm, and enhance the optimization ability of the algorithm. Combined with the three evaluation indicators of assembly geometric feasibility, assembly process continuity, and assembly tool change times, a fitness function is constructed to achieve multi-objective optimization. Finally, experiments show that the multi-agent evolutionary algorithm is incorporated into the planning process to obtain an accurate solution through the various population strategy-particle swarm optimization algorithm, which proves the feasibility of the compound algorithm and has better performance in solving assembly

关键词： Assembly sequence planning particle swarm optimization algorithm Various population strategy Hybrid algorithm

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Discrete particle swarm optimization algorithm for Lot-streaming No-wait Flow Shop Scheduling Problem

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2nd International Conference on Advanced Engineering Materials and Technology (AEMT)

作者： Yang, Lin Pan, Yuxia Hainan Univ Sanya Coll Sanya 572022 Hainan Peoples R China

ISBN: (纸本)9783037854471

This paper proposed discrete particle swarm optimization(DPSO) algorithm to solve lot-streaming no-wait flow shop scheduling problem(LNFSP) with the objective of the maximum completion time. The natural encoding scheme based on job permutation and newly-designed methods were adopted to produce new individuals. After the DPSO-based exploration, a efficient fast local search based on swap neighborhood structure is used to enhance the exploitation capability. Simulation results show the effectiveness of the proposed algorithms.

关键词： particle swarm optimization algorithm lot-streaming no-wait flow shop scheduling problem local search

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