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Research on secure encryption method of multi-domain fiber network based on particle swarm optimization algorithm

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第1期38卷 139-145页

作者： Zeng, Jun Yangtze Normal Univ Coll Big Data & Intelligent Engn Chongqing Peoples R China

In order to improve the security of information storage in multi-domain optical network privacy protection, hybrid encoding and encryption of privacy protection information in multi-domain optical network is carried out. A hybrid coding and secure encryption technique based on particle swarm optimization (PSO) for privacy protection information in multidomain optical network is proposed. The coding mapping equation of privacy protection information security encryption in multi-domain optical network is constructed, and the privacy protection information of multi-domain fiber network is loaded into the coding mapping equation of security encryption of privacy protection information in multi-domain fiber network. A set of characteristic solutions describing the entropy function of the characteristic distribution of random encryption keys are obtained, and the elliptic mapping random linear combination coding and chaotic encryption key allocation are carried out. The privacy protection method of multi-domain optical fiber network is used to encrypt and decode information with mixed coding and steganography. Under the bilinear mapping coding system, the privacy-protected information in multidomain optical network is encrypted and encrypted, stored and transmitted confidently. The simulation results show that this encryption technique has better steganography performance and better secure transmission and storage performance for the privacy protection information mixed encoding encryption in multi-domain optical network.

关键词： particle swarm optimization algorithm multi-domain fiber optic network secure encryption

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Multi-objective optimization of double-walled steel cofferdams based on response surface methodology and particle swarm optimization algorithm

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STRUCTURES 2023年 49卷 256-266页

作者： Shao, Junhu Fan, Zihao Huang, Yuanyuan Zhan, Yulin Cai, Qinhao Chengdu Univ Sch Architecture & Civil Engn Chengdu 610106 Peoples R China Southwest Jiaotong Univ Dept Bridge Engn Chengdu 610031 Peoples R China Southwest Jiaotong Univ Inst Civil Engn Mat Chengdu 610031 Peoples R China China Railway Erju Fifth Engn Co Ltd Chengdu 610031 Peoples R China

To improve the safety of steel cofferdams and reduce the cost to the extent possible, a multi-objective optimi-zation method based on response surface methodology (RSM) and particle swarm optimization (PSO) algorithm was proposed to optimize the structure of double-walled steel cofferdams in the deep-water bridge foundation construction. The thickness of the inner and outer wall plate, the height and thickness of the inner-wall brace and the vertical spacing between annular plates were selected as design variables, taking the stress of the inner-wall brace and inner wall plate as the optimization objectives and the cost as the constraint condition, an optimization model of double-wall steel cofferdam was proposed. On the basis of the finite element calculation results, the experimental design and RSM were used to obtain the explicit relationship between the design variables and the structural response of the cofferdam. The optimal values of the design parameters of the cofferdam were obtained using PSO algorithm. The optimal design parameters and the structural response of the cofferdam variation with the cost were investigated. It is shown that the combination of RSM and PSO is able to optimize the cofferdam structure, with a stress optimization rate of 49.8% for the inner-wall brace while keeping the original cost constant. In addition, by adjusting the cost threshold, the reasonable range of cost coefficient K for this cofferdam is 0.119 to 0.162, and the section size of inner-wall brace is the key design parameter for the cofferdam, which provides a design idea for the optimization of deep-water cofferdam structure.

关键词： Double-walled steel cofferdam Response surface methodology Constraints particle swarm optimization algorithm Multi-objective optimization design

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Network Scheduling Model of Cloud Computing based on particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2015年第4期8卷 73-81页

作者： Lu, Ke Meng, Junxia Jiaozuo Univ Dept Int Educ Jiaozuo 454003 Peoples R China Jiaozuo Univ Coll Informat Engn Jiaozuo 454003 Peoples R China

The paper proposed a network scheduling in cloud computing based on intelligence particle swarm optimization algorithm aimed at the disadvantages of cloud computing network scheduling. Firstly, on the basis of cloud model, used intelligence particle swarm optimization algorithm with strong ability of global searching to find the better solution of cloud computing network scheduling then turned the better solution into the initial pheromone of improved particle swarm optimization algorithm, and found out the cloud computing network scheduling and the algorithm's global optimal solution through improved particle swarm optimization information communications and feedbacks. Finally, made comparison test of the three benchmark function on the basis of MATLAB, the results showed, compared with traditional intelligence particle swarm optimization algorithms, the improved algorithm can preferably allocate the resources in cloud computing model, the effect of prediction model time is more close to actual time, can efficiently limit the possibility of falling into local convergence, the optimal solution's time of objective function value is shorten which meet the user's needs more.

关键词： Network Computing Cloud Computing particle swarm optimization algorithm Network Scheduling

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A New particle swarm optimization algorithm to Hierarchy Multi-objective optimization Problems and Its Application in Optimal Operation of Hydropower Stations

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JOURNAL OF COMPUTERS 2012年第8期7卷 2039-2046页

作者： Yang, Junjie Zhanjiang Normal Univ Sch Informat & Technol Zhanjiang Peoples R China

Some engineering optimization problems, such as multi-objective daily generation scheduling for the hydropower stations, the objective functions has obvious hierarchy and priority, simultaneously, the constraints is very complex. It is difficult to solve the operation problems by adopting traditional optimal technique. In this paper, a new particle swarm optimization algorithm solving hierarchy multi-objective timization problems is proposed. The algorithm can handle the level multi-objective optimization problem effectively. By adopting the adaptive inertia weight algorithm (AWA) and mutative scale local search algorithm (MSLSA), the convergence performance of the algorithm is improved. Then, A multi-objective daily generation scheduling model for the hydropower stations is established, in which two objective functions including maximization of peak-energy capacity benefits and maximization of power generation are involved. Finally, Multi-objective daily generation scheduling problem of the Three Gorges cascade hydropower system during low-flow period is studied with proposed algorithm to obtain the maximum peak-energy capacity benefits, as well as power generation benefits of three gorges cascade stations.

关键词： multi-objective optimization particle swarm optimization algorithm daily generation scheduling

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Application of the particle swarm optimization algorithm-back propagation neural network algorithm introducing new parameter terms in the application field of industrial design

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RESULTS IN ENGINEERING 2024年 21卷

作者： Tian, Yuan Chang, Yu North China Inst Aerosp Engn Sch Art & Design Langfang 065000 Peoples R China Tianjin Univ Commerce Sch Arts Tianjin 300134 Peoples R China

With the development of the modern industry, to continuously use large precision instruments, the circuits protecting method through circuit design has gradually been promoted. And this method can prevent the excessive accumulation of electric heat from causing fires. The voltage at the starting point of power transmission is constant. However, during the transportation process, some lines have low resistance values, which can generate large instantaneous currents. To address this issue, this study conducted simulation experiments on selfcollected Circuit dataset based on particle swarm optimization and backpropagation neural networks. This study first introduced new parameter term learning factors into backpropagation neural networks. Then they were imported into the support vector machine, and the nonlinear variables were mapped to high plane, and the optimal hyperplane was established. Then the traditional circuit design method was improved, 40 Resistor were connected in parallel and connected to the experimental circuit in series with the rheostat. Finally, the algorithm was introduced into Circuit dataset collected in this experiment, and its protective effect on the circuit was compared with the other three algorithms. Under the protection of this design, the working times of four algorithms were 0.28, 0.42, 0.38, and 0.43 s, respectively. Their phase displacements were 0.19, 0.26, 0.36, and 0.41, respectively. The circuit design method proposed in this study can effectively address circuit faults. And the fusion algorithm can disconnect the circuit at the fastest speed and significantly reduce excitation current intensity, and it is suitable for circuit design in the field of industrial design.

关键词： Adaptive learning Support vector machine particle swarm optimization algorithm Voltage sag Circuit protection design

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Hybridizing salp swarm algorithm with particle swarm optimization algorithm for recent optimization functions

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EVOLUTIONARY INTELLIGENCE 2022年第1期15卷 23-56页

作者： Singh, Narinder Singh, S. B. Houssein, Essam H. Punjabi Univ Dept Math Patiala Punjab India Minia Univ Fac Comp & Informat El Minia Governorate Egypt

The salp swarm algorithm (SSA) has shown its fast search speed in several challenging problems. Research shows that not every nature-inspired approach is suitable for all applications and functions. Additionally, it does not provide the best exploration and exploitation for each function during the search process. Therefore, there were several researches attempts to improve the exploration and exploitation of the meta-heuristics by developing the newly hybrid approaches. This inspired our current research and therefore, we developed a newly hybrid approach called hybrid salp swarm algorithm with particle swarm optimization for searching the superior quality of optimal solutions of the standard and engineering functions. The hybrid variant integrates the advantages of SSA and PSO to eliminate many disadvantages such as the trapping in local optima and the unbalanced exploitation. We have used the velocity phase of the PSO approach in salp swarm approach in order to avoid the premature convergence of the optimal solutions in the search space, escape from ignoring in local minima and improve the exploitation tendencies. The new approach has been verified on different dimensions of the given functions. Additionally, the proposed technique has been compared with a wide range of algorithms in order to confirm its efficiency in solving standard CEC 2005, CEC 2017 test suits and engineering problems. The simulation results show that the proposed hybrid approach provides competitive, often superior results as compared to other existing algorithms in the research community.

关键词： Standard functions Heuristic hybridization Salp swarm algorithm particle swarm optimization algorithm Exploration and exploitation

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Based on particle swarm optimization algorithm of Cloud Computing Resource Scheduling in Mobile Internet

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第6期9卷 25-34页

作者： Lin, Yong Qingdao Vocat & Tech Coll Hotel Management Qingdao Shandong Peoples R China

Mobile Internet due to the limitation of the mobile terminal power supply, transmission and calculation of the need to adopt energy saving strategy, also due to the terminal mobility, mobile Internet topology change. Therefore, mobile Internet cloud computing resources allocation need both energy efficiency and assure the time characteristics of mobile business;Aiming at this problem, this paper presents a maximum energy efficiency optimization, in the restrictive conditions at the same time, guarantee the minimum time delay the business. According to the characteristics of the optimization problems, both the distribution of the improved algorithm is proposed, the algorithm based on particle swarm optimization (pso) algorithm, build the search direction matrix of orientation, the simulation results show that the proposed allocation algorithm can effectively improve the efficiency of energy utilization, and ensure that the time delay of the business requirements.

关键词： The Mobile Internet Cloud Computing particle swarm optimization algorithm Resource Scheduling

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Optimal Energy Consumption optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid Gravitational Search and particle swarm optimization algorithm

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Energy Engineering 2022年第6期119卷 2489-2511页

作者： Rongxin Zhang Chengying Yang Xuetao Li School of Economics and Management Hubei University of Automotive TechnologyShiyan442000China School of Management Universiti Sains MalaysiaPenang11800Malaysia School of Science Hubei University of Automotive TechnologyShiyan442002China

Buildings are the main energy consumers across the world,especially in urban *** smartization,or the smartification of housing,therefore,is a major step towards energy grid smartization *** controlling the energy consumption of lighting,heating,and cooling systems,energy consumption can be *** or some part of the energy consumed in future smart buildings must be supplied by renewable energy sources(RES),which mitigates environmental impacts and reduces peak demand for electrical *** this paper,a new optimization algorithm is applied to solve the optimal energy consumption problem by considering the electric vehicles and demand response in smart *** this way,large power stations that work with fossil fuels will no longer be *** current study modeled and evaluated the performance of a smart house in the presence of electric vehicles(EVs)with bidirectional power exchangeability with the power grid,an energy storage system(ESS),and solar ***,the solar RES and ESS for predicting solar-generated power prediction uncertainty have been considered in this *** case studies,including the sales of electrical energy resulting from PV panels’generated power to the power grid,time-variable loads such as washing machines,and different demand response(DR)strategies based on energy price variations were taken into account to assess the economic and technical effects of EVs,BESS,and solar *** proposed model was simulated in MATLAB.A hybrid particle swarm optimization(PSO)and gravitational search(GS)algorithm were utilized for *** generation and reduction were performed via LHS and backward methods,*** results demonstrate that the proposed model minimizes the energy supply cost by considering the stochastic time of use(STOU)loads,EV,ESS,and PV *** on the results,the proposed model markedly reduced the electricity costs of the smart house.

关键词： Energy management smart house particle swarm optimization algorithm gravitational search algorithm demand response electric vehicle

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Active Distribution Network Fault Location Based on Petri Nets and Improved particle swarm optimization algorithm

ELECTRICA

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ELECTRICA 2024年第3期24卷 789-798页

作者： Xia, Yunfeng Yang, Jie Chen, Junan Lai, Yeming Ye, Sheng Hainan Power Grid Co Ltd Transmiss Maintenance Branch Haikou Hainan Peoples R China

To address issues such as inadequate fault tolerance, long computation time, and limited universality in fault location for active distribution networks, this paper proposes a fault location method that combines Petri nets with an improved particle swarm optimization (PSO) algorithm. This method enhances the efficiency of fault location in distribution networks with distributed power sources, demonstrating good applicability and convergence, especially in complex network scenarios. The results from two test functions and simulation analyses of two types of node distribution networks show that (1) in the single-point fault simulation, the improved algorithm successfully located the fault in the ninth iteration, outperforming the 14th iteration result of the standard PSO algorithm. (2) In networks with randomly interconnected distributed power sources, the algorithm accurately located both single and multiple faults. (3) Experimental verification further supports the simulation results, proving the effectiveness of this method in practical applications.

关键词： Active distribution network fault location Petri nets particle swarm optimization algorithm

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A Comprehensively Improved particle swarm optimization algorithm to Guarantee particle Activity

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RUSSIAN PHYSICS JOURNAL 2021年第5期64卷 866-875页

作者： Bi, Ya Lam, Anthony Quan, Huiqun Liu, Hui Wang, Cunfa Hubei Univ Econ Sch Business Adm Wuhan Peoples R China Huazhong Univ Sci & Technol Coll Publ Adm Wuhan Peoples R China Katholieke Univ Leuven Fac Econ & Business Leuven Belgium Wuhan Univ Technol Sch Management Wuhan Peoples R China Fujian Zhuozhi Project Investment Consulting Co L Wuhan Peoples R China

The particle swarm optimization (PSO) algorithm has certain disadvantages;for instance, the convergence viscosity of the algorithm is reduced in the post evolution phase, the optimization search efficiency is also reduced, the algorithm is easy to be inserted with a local extremum during the calculation of a complex problem of a high-dimensional multiple extremum, and the convergence thereof is low. To compensate for the PSO disadvantage, we propose a particle swarm optimization of the comprehensive improvement strategy, which is a simple particle swarm optimization with a dynamic adaptive hybridization of the extremum disturbance and the ecds-PSO algorithm. This new comprehensively improved particle swarm algorithm discards the particle velocity and reduces the PSO from the second-order to a first-order difference equation. The evolutionary process is controlled by the particle position variables only. The hybridization operation of increasing the extremum disturbance and introducing a genetic algorithm can accelerate the particles to overstep the local extremum. The mathematical derivation and the plurality of a comparative experiment provide the following information: the improved particle swarm optimization is a simple and effective optimization algorithm that can enhance the algorithm accuracy, the convergence viscosity and the ability of avoiding the local extremum, and effectively reduce the calculation complexity.

关键词： particle swarm optimization algorithm dynamic adaptive hybridization local extremum particle activity

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