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A new deterministic PSO algorithm for real-time systems implemented on low-power devices

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2023年 429卷

作者： Dlugosz, Zofia Rajewski, Michal Dlugosz, Rafal Talaska, Tomasz Pedrycz, Witold Bydgoszcz Univ Sci & Technol Fac Telecommun Comp Sci & Elect Engn ul Al Prof S Kaliskiego 7 PL-85796 Bydgoszcz Poland Aptiv Serv Poland Podgorki Tynieckie 2 PL-30399 Krakow Poland Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada Polish Acad Sci Syst Res Inst PL-00901 Warsaw Poland Istinye Univ Fac Engn & Nat Sci Dept Comp Engn Istanbul Turkiye King Abdulaziz Univ Comp Engn Fac Engn Dept Elect Jeddah 21589 Saudi Arabia

The paper presents a novel, deterministic particle swarm optimization (PSO) algorithm, facilitating its implementation in low-power application specific integrated circuits (ASIC) realized in CMOS technology. The PSO algorithms are commonly used in a variety of optimization problems. They allow to search for a global extreme in a selected fitness function, which is used for numerical modeling of specific optimized problems. The conventional version of the algorithm is relatively difficult to implement directly in hardware, especially when each particle is represented by a separate circuit working in parallel with other particles. In such a situation, each particle has to be equipped with its own copy of each function. One of the problems is, for example, an efficient realization of the random function block that provides for each particle some factors used in computing a cognitive and a social velocity components. In this paper we shows a novel way to work around this problem. In the proposed approach the random function does not need to be used. It has been replaced by a simple computationally deterministic mechanism, which at the level of the overall swarm allows for effective emulation of the former random mechanism. We proposed such simplifications that reduce the formulas describing the algorithm to only basic arithmetic and logic operations. Comprehensive investigations have shown that performance of the proposed approach is comparable with its original version or in may situations even better. The proposed algorithm was tested with numerous test functions (50 runs for each tested function), to verify its flexibility and adaptation abilities to different problems.(c) 2023 Elsevier B.V. All rights reserved.

关键词： particle swarm optimization algorithm Parallel computing Deterministic approach Hardware implementation Low power circuits CMOS technology

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Thermodynamic Multi-Field Coupling optimization of Microsystem Based on Artificial Intelligence

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MICROMACHINES 2023年第2期14卷 411-411页

作者： Shan, Guangbao Wu, Xudong Li, Guoliang Xing, Chaoyang Zhang, Shengchang Fu, Yu Xidian Univ Sch Microelect Xian 710071 Peoples R China Beijing Inst Aerosp Control Devices Beijing 100039 Peoples R China China Acad Aerosp Standardizat & Prod Assurance Beijing 100071 Peoples R China

An efficient multi-objective optimization method of temperature and stress for a microsystem based on particle swarm optimization (PSO) was established, which is used to map the relationship between through-silicon via (TSV) structural design parameters and performance objectives in the microsystem, and complete optimization temperature, stress and thermal expansion deformation efficiently. The relationship between the design and performance parameters is obtained by a finite element method (FEM) simulation model. The neural network is built and trained in order to understand the mapping relationship. Then, the design parameters are iteratively optimized using the PSO algorithm, and the FEM results are used to verify the efficiency and reliability of the optimization methods. When the optimization target of peak temperature, bump temperature, TSV temperature, maximum stress and maximum thermal deformation are set as 100 degrees C, 55 degrees C, 35 degrees C, 180 Mpa and 12 mu m, the optimization results are as follows: the peak temperature is 97.90 degrees C, the bump temperature is 56.01 degrees C, the TSV temperature is 31.52 degrees C, the maximum stress is 247.4 Mpa and the maximum expansion deformation is 11.14 mu m. The corresponding TSV structure design parameters are as follows: the radius of TSV is 10.28 mu m, the pitch is 65 mu m and the thickness of SiO2 is 0.83 mu m. The error between the optimization result and the target temperature is 2.1%, 1.8%, 9.9%, 37.4% and 7.2% respectively. The PSO method has been verified by regression analysis, and the difference between the temperature and deformation optimization results of the FEM method is not more than 3%. The stress error has been analyzed, and the reliability of the developed method has been verified. While ensuring the accuracy of the results, the proposed optimization method reduces the time consumption of a single simulation from 2 h to 70 s, saves a lot of time and human resources, greatly improves

关键词： TSV optimization particle swarm optimization algorithm microsystem

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How to realize the power demand side actively matching the supply side? --A virtual real-time electricity prices optimization model based on credit mechanism

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APPLIED ENERGY 2023年 343卷

作者： Guo, Zhilong Xu, Wei Yan, Yue Sun, Mei Jiangsu Univ Ctr Energy Dev & Environm Protect Zhenjiang 212013 Jiangsu Peoples R China

Intermittence and fluctuation of renewable energy increase the cost of real-time balance of power supply and demand, which will affect the stability of the power grid. How to design the incentive mechanism to increase the flexibility of the distributed energy system and reduce the cost of real-time supply-demand balance is an urgent problem to be solved. In this study, from the perspective of the demand side actively matching the supply side, a virtual real-time electricity price optimization model based on the credit mechanism is proposed. We use the linear decreasing inertia weight particle swarm optimization algorithm to optimize the user's electricity price and realize the minimization of the total electricity cost. The influence of the virtual real-time electricity price incentive mechanism on the matching degree between the power load curve and the distributed energy output curve is also studied. Moreover, a novel index is put forward to quantitatively measure the matching degree between users' power load curve and distributed energy output curve. Taking a multi-microgrid system in a China's industrial park as the case, we have found the results that the virtual real-time electricity price optimization model can not only reduce users' total power cost by 7.8108%, but also improve the matching degree between the demand side and the supply side by 2.4643%. The new demand response incentive mechanism we have presented in this paper may promote the effective utilization of distributed energy and reduce the impact of renewable energy on power grid.

关键词： Distributed energy Virtual real-time electricity price Demand side matches supply side particle swarm optimization algorithm

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Design of improved four-coil structure with high uniformity and effective coverage rate

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HELIYON 2023年第4期9卷 e15193页

作者： Zhu, Xuehua Liu, Chuangchuang Su, Hao Miao, Yutong Cheng, Hongde AnHui Polytech Univ AnHui Key Lab Detect Technol & Energy Saving Devic Wuhu 241000 Peoples R China

Helmholtz coils have extensive applications in biological medicine, aerospace, and other in-dustries depending on the simple structure and miraculous magnetic field characteristics. How-ever, the uniform zone generated by them is not appropriate for scientific experiments with large devices. Due to the limitations of Helmholtz coils in application, a novel design technique is proposed to improve the homogeneity and region of magnetic field. The main approach is to add an auxiliary coil on each side of Helmholtz coils to compensate for the magnetic field that exists farther out from the center point. To analyze the size relationship between the auxiliary coil and the main coil to obtain the best magnetic field distribution, the traditional Maclaurin expansion method and particle swarm optimization (PSO) algorithm are used to research and discuss. The magnetic field distribution and the corresponding effective coverage rate (ECR) of the improved schemes with different structural parameters are calculated under the relative deviations of 0.1%, 0.5% and 1%, respectively. The results obtained by the above optimization methods are verified by the finite element software COMSOL and specific experiments. Both optimization methods manifest that the maximum effective coverage rate can be achieved when the size of the auxiliary coil is consistent with that of the main coil. In addition, we compare the improved four-coil structure proposed in this paper with the existing four-coil square structure under the same volume. The data show that the improved structure has certain advantages in the spatial magnetic field distribution. The corresponding tri-axial coil system is established by adopting the param-eters on the single axis, which can achieve a constant magnetic field in arbitrary directions by controlling the magnitude and direction of current on each axis. This provides a theoretical basis for the application of magnetic navigation technology.

关键词： Uniform magnetic field Maclaurin expansion method particle swarm optimization algorithm Effective coverage rate Finite element simulation

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Fast reconstruction method for defect profiles of ferromagnetic materials based on metal magnetic memory technique

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MEASUREMENT 2023年 215卷

作者： Li, Junting Su, Sanqing Wang, Wei Liu, Xinwei Zuo, Fuliang Xian Univ Architecture & Technol Sch Civil Engn Xian 710055 Shaanxi Peoples R China

In this paper, a fast reconstruction method for surface defect profiles of ferromagnetic materials is proposed based on the metal magnetic memory technology. An improved magnetic charge model that can adapt to rectangular and V-shaped defect profiles and a new particle swarm optimization algorithm based on a chaotic initial distribution, sigmoid inertia weight coefficient, and sine cosine acceleration coefficients are established as the forward model and iterative means of the method, respectively. The proposed method is verified with theoretical and experimental data, and the influence of noise is considered. The reconstruction method has good accuracy, repeatability, and robustness.

关键词： Metal magnetic memory Defect reconstruction Magnetic charge model particle swarm optimization algorithm

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Calculation and optimization of inter-regional available transfer capability considering wind power uncertainty and system improvement measures

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Liu, Wenli Zhang, Tao Yang, Xiaolei Wang, Jin China Three Gorges Univ Coll Elect Engn & New Energy Yichang Hubei Peoples R China State Grid Jiaxing Power Supply Co Jiaxing Zhejiang Peoples R China

The intermittency and uncertainty of wind energy make transmission congestion management increasingly complex. In this paper, we investigate the impact of wind energy on available transmission capacity (ATC) and then explore ways to mitigate transmission congestion from the grid and load side, respectively. The impact of a stochastic variable is considered by applying Latin Hypercube Sampling (LHS) and backward curtailment techniques to generate typical scenarios. ATC is improved by the optimal allocation of Thyristor Controlled Series Compensation (TCSC) on the grid side and the use of Demand Response (DR) on the load side. The source-grid-load cooperative optimisation model for ATC is solved by an improved particle swarm optimisation (PSO) algorithm. Based on the IEEE-30 bus system, an experimental scheme is designed and analytical calculations are performed. The results show that the joint application of TCSC and DR can help to improve ATC in a comprehensive way. The work done in this paper can achieve the purpose of promoting renewable energy consumption by improving the utilisation efficiency of the transmission network without changing the network structure.

关键词： wind power's stochastic characteristics available transfer capability demand response thyristor-controlled series compensation particle swarm optimization algorithm

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Research on data mining algorithm based on neural network and particle swarm optimization

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第3期35卷 2921-2926页

作者： Fei, Xianju Tian, Guozhong Changzhou Inst Technol Sch Comp Informat & Engn Changzhou Jiangsu Peoples R China

In previous studies, due to the sparsity and chaos of distributed data, such a result would lead to a local convergence phenomenon by using PSO algorithm, resulting in low accuracy of data mining. So this time we proposed a data mining algorithm based on neural network and particle swarm optimization. At the beginning, we calculated the global kernel function of differentiated distributed data mining and mixed to build the mining decision model. The training error was used as the constraint condition of mining optimization to realized data optimization mining. The results showed that the differential distributed data mining with this algorithm has higher accuracy and stronger convergence.

关键词： Neural network algorithm particle swarm optimization algorithm data mining algorithm

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Network traffic prediction model based on improved VMD and PSO-ELM

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2023年第7期36卷

作者： Shi, Jinmei Zhou, Jinghe Feng, Junying Chen, Huandong Hainan Vocat Univ Sci & Technol Coll Informat Engn Haikou Peoples R China Hainan Normal Univ Coll Informat Sci & Technol Haikou Peoples R China Hainan Vocat Univ Sci & Technol Coll Informat Engn Haikou 571126 Hainan Peoples R China

The rapid update of computing power leads to exponential data traffic growth, and the incidence of network attacks is also increasing. It is significantly important to analyze and predict network traffic accurately in the early stage and take corresponding preventive measures. The existing network flow integrated forecasting models still have some bottlenecks that are difficult to solve, for example, the slow optimization speed of modal decomposition parameters, easy falling into local optimal solutions, the slow convergence speed of the training process, and poor generalization capability. In this paper, particle swarm optimization (PSO) is utilized to improve the parameters selection process of the variational mode decomposition (VMD) algorithm and the extreme learning machine (ELM) algorithm. First, the PSO-VMD combined with multi-scale permutation entropy (MPE) is utilized to decompose the original network flow, and multiple eigenmode components are obtained. Second, the PSO-ELM is utilized to train the network traffic prediction model, and the PSO parameters in PSO-ELM are updated through adaptive weight adjustment and synchronous learning factors to increase the training and prediction speed, and the component prediction results are reconstructed to get a high-precision network flow forecasting result. Finally, through the prediction and verification of the public network flow data of the WIDE backbone, the result of this experiment indicates that the VMD-PSO-ELM can break through the bottlenecks of slow optimization speed of VMD decomposition parameters, reduce the computational complexity of ELM, accelerate the convergence speed, and increase the forecasting accuracy.

关键词： extreme learning machine multi-scale permutation entropy network traffic prediction particle swarm optimization algorithm variational mode decomposition

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Research on Financial Risk Warning Based on Intelligent algorithms 24

Research on Financial Risk Warning Based on Intelligent Algo...

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2024 5th International Conference on Big Data Economy and Information Management, BDEIM 2024

作者： Mou, Yanrong School of Business Wuhan Qingchuan University Hubei Wuhan China

ISBN: (纸本)9798400711862

Financial risk early warning has always been one of the important research topics in corporate management decision-making. Timely discovering the existence of financial risks and resolving or avoiding risks in advance can reduce the probability of enterprises falling into financial crisis and avoid certain investment losses for investors. Aiming at the imbalanced sample characteristics of financial risk early warning scenarios, this paper starts from the perspective of improving sample imbalance and takes the construction of a classification model with good generalization performance as the goal to carry out research on financial risk early warning models. To this end, this study uses the particle swarm algorithm to improve the initial weights and thresholds of the neural network, and then uses the optimal solution obtained by the algorithm as the new weights and thresholds of the network. Finally, the fully trained network is used to predict and simulate the financial risk level of listed companies. The research results show that the prediction accuracy of the financial risk early warning model based on the PSO-BP neural network reaches 86.4%, and through the robustness test of the model, it shows that the financial risk early warning model constructed in this paper is reasonable and has application value;by comparing the prediction results with the early warning models based on BP neural network and GA-BP neural network algorithm, it is found that the PSO-BP neural network early warning model is superior to the other two models and has higher prediction accuracy, which can better predict corporate financial risks. © 2024 Copyright held by the owner/author(s).

关键词： BP neural network Electronic information industry Financial warning particle swarm optimization algorithm

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Optimal Sizing of Battery and Super-Capacitor Based on the MOPSO Technique via a New FC-HEV Application

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ENERGIES 2023年第9期16卷

作者： Djouahi, Abdeldjalil Negrou, Belkhir Rouabah, Boubakeur Mahboub, Abdelbasset Samy, Mohamed Mahmoud Univ Kasdi Merbah Ouargla Lab Promot & Valorisat Ressources Sahariennes VPRS BP 511 Ouargla 30000 Algeria Univ Kasdi Merbah Ouargla Elect Engn Dept BP 511 Ouargla 30000 Algeria Beni Suef Univ Fac Engn Elect Engn Dept Bani Suwayf 2722165 Egypt

In light of the energy and environment issues, fuel cell vehicles have many advantages, including high efficiency, low-temperature operation, and zero greenhouse gas emissions, making them an excellent choice for urban environments where air pollution is a significant problem. The dynamics of fuel cells, on the other hand, are relatively slow, owing principally to the dynamics of the air compressor and the dynamics of manifold filling. Because these dynamics can limit the overall performance of fuel cell vehicles, two key technologies that have emerged as critical components of electric vehicle powertrains are batteries and supercapacitors. However, choosing the best hybrid energy storage system that combines a battery and a supercapacitor is a critical task nowadays. An electric vehicle simulated application by MATLAB Code is modeled in this article using the multi-objective particle swarm optimization technique (MOPSO) to determine the appropriate type of batteries and supercapacitors in the SFTP-SC03 drive cycle. This application optimized both component sizing and power management at the same time. Batteries of five distinct types (Lithium, Li-ion, Li-S, Ni-Nicl2, and Ni-MH) and supercapacitors of two different types (Maxwell BCAP0003 and ESHSR-3000CO) were used. Each storage component is distinguished by its weight, capacity, and cost. As a consequence, using a Li-ion battery with the Maxwell BCAP0003 represented the optimal form of hybrid storage in our driving conditions, reducing fuel consumption by approximately 0.43% when compared to the ESHSR-3000CO.

关键词： fuel-cell hybrid electric vehicle particle swarm optimization algorithm hydrogen consumption multi-objective function problem energy management strategy

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