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Optimized FOPID controller for nuclear research reactor using enhanced planet optimization algorithm

ALEXANDRIA ENGINEERING JOURNAL 2024年 97卷 267-282页

作者： Abdelfattah, Hany Aseeri, Ahmad O. Abd Elaziz, Mohamed Suez Univ Fac Technol & Educ Elect Dept Suez 41522 Egypt Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Al Kharj 11942 Saudi Arabia Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Galala Univ Fac Comp Sci & Engn Suze 435611 Egypt Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman 346 U Arab Emirates Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Middle East Univ MEU Res Unit Amman 11831 Jordan

Nuclear reactor control is pivotal for the safe and efficient operation of nuclear power plants, facilitating the regulation of reactor reactivity. This study introduces an optimized fractional-order proportional-integral-derivative (FOPID) controller tailored for maintaining reactivity levels in nuclear power plants, particularly during load-following operations. The controller adjusts the position of control rod to regulate power output effectively. We enhance FOPID controller's performance using a modification of Planet optimization algorithm (POA-M), leveraging the strengths of the Arithmetic optimization algorithm (AOA) to improve its exploitation capabilities. We evaluate the efficacy of POA-M-FOPID controller against traditional techniques, including POA, AOA, and particle swarm optimization (PSO). We assess its performance using the Egyptian Testing Research Reactor (ETRR-2) as a case study. Our results demonstrate that the POA-M-FOPID controller outperforms alternative algorithms across various control metrics, exhibiting superior resilience and efficiency. Notably, the utilization of the POA-M-FOPID controller yields remarkable improvements in reactor power performance, achieving significantly reduced settling time (25.27 sec) and maximum overshoot (0.67 %) compared to conventional FOPID controllers incorporating POA, AOA, and PSO methods. These findings underscore the effectiveness of POA-MFOPID in enhancing nuclear reactor control systems, offering potential benefits for broader nuclear power industry in terms of safety, stability, and operational efficiency.

关键词： Planet optimization algorithm Arithmetic optimization algorithm particle swarm optimization algorithm fractional-order proportional-integral-deriva tive (FOPID) controller

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Inatorial forecasting method considering macro and micro characteristics of chaotic traffic flow

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Chinese Physics B 2023年第10期32卷 350-362页

作者：侯越张迪李达杨萍 School of Electronics and Information Engineering Lanzhou Jiaotong UniversityLanzhou 730070China

Traffic flow prediction is an effective strategy to assess traffic conditions and alleviate traffic congestion. Influenced by external non-stationary factors and road network structure, traffic flow sequences have macro spatiotemporal characteristics and micro chaotic characteristics. The key to improving the model prediction accuracy is to fully extract the macro and micro characteristics of traffic flow time sequences. However, traditional prediction model by only considers time features of traffic data, ignoring spatial characteristics and nonlinear characteristics of the data itself, resulting in poor model prediction performance. In view of this, this research proposes an intelligent combination prediction model taking into account the macro and micro features of chaotic traffic data. Firstly, to address the problem of time-consuming and inefficient multivariate phase space reconstruction by iterating nodes one by one, an improved multivariate phase space reconstruction method is proposed by filtering global representative nodes to effectively realize the high-dimensional mapping of chaotic traffic flow. Secondly, to address the problem that the traditional combinatorial model is difficult to adequately learn the macro and micro characteristics of chaotic traffic data, a combination of convolutional neural network(CNN) and convolutional long short-term memory(ConvLSTM) is utilized for capturing nonlinear features of traffic flow more comprehensively. Finally,to overcome the challenge that the combined model performance degrades due to subjective empirical determined network parameters, an improved lightweight particle swarm is proposed for improving prediction accuracy by optimizing model hyperparameters. In this paper, two highway datasets collected by the Caltrans Performance Measurement System(PeMS)are taken as the research objects, and the experimental results from multiple perspectives show that the comprehensive performance of the method proposed in this

关键词： traffic flow prediction phase space reconstruction particle swarm optimization algorithm deep learning models

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Design and analysis of energy efficient urban buildings based on BIM model

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第6期24卷 3785-3798页

作者： Wu, Yang Chongqing Business Vocat Coll Dept Publishing Media Chongqing 401331 Peoples R China

Traditional buildings face problems such as low construction efficiency and high costs during the construction process, which cannot meet the requirements of green and sustainable development of buildings. To address this, a design model is constructed using BIM technology as a framework, which optimizes the energy efficiency of multiple building indicators and constructs a building energy efficiency optimization model based on decomposition of multiple objectives. Considering the cost and convergence issues of the goal solving model, an agent-assisted multi-objective particle swarm optimization model is introduced to construct an energy-saving design optimization model. The fitness of the particle swarm is optimized through an agent optimization strategy, thereby adjusting the speed and position of particles and improving the model optimization effect. Performed performance testing on the proposed model, and the proposed model has the best optimization performance under the Ackley function. After 100 iterations, it tends to converge. At this point, the best fitness value of the model is 0.452. Applying the proposed technology to specific cases for target optimization of urban single-room office buildings, the proposed optimization model has a shorter search time, a minimum of 2408 uncomfortable hours, and a minimum total energy consumption of 47.5, which are superior to the other three models. Finally, by comparing the comprehensive application effects of the model, the proposed model has the best performance in terms of super volume index, reaching 26,473 in urban office buildings. Compared to the comparison model, the proposed model takes 1.3 h, and the overall optimization time is the shortest. In traditional residential building testing, the proposed model has a super volume index of 50,132, which is also the best and has the shortest training time. It can be seen that the proposed optimization model has excellent optimization ability, and compared to other tec

关键词： BIM energy saving architecture particle swarm optimization algorithm multiple objectives simulation

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optimization process of designing and manufacturing of cylinder block system in cast iron industry using metaheuristic computing technique

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第3期19卷 2203-2222页

作者： Urvashi, Shikha Bansal, Shikha SRM Inst Sci & Technol Dept Math Delhi NCR Campus Ghaziabad 201204 India

A cylinder block system is a complicated component within a cast iron manufacturing structure, involving several interaction subsystems throughout the production process. Given the increasing need for efficient automotive components, optimizing the design and manufacturing processes of this system to enhance its performance and production efficiency is imperative. The primary aim of this work is to design and enhance the performance of the cylinder block system utilizing a hybrid metaheuristic approach, specifically the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The algorithms are executed in MATLAB-R2022 software for the computation of numerical results for the performance of the system. The system's mathematical model is constructed using the Markov birth-death process, and the differential associated with that model is generated from the system's state transition diagram. The Markov birth-death technique examines the system's performance and determines which subsystem is more critical and needs greater maintenance. The system's performance is enhanced through the utilization of the PSO and GA algorithms. The system's optimism parameters value is determined using these algorithms, varying both population size and generation size;these optimal parameters help the casting production industry to enhance system performance.

关键词： Design Genetic algorithm Maintenance Markov approach Performance particle swarm optimization algorithm Steady-state availability

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PSO-GA Hyperparameter Optimized ResNet-BiGRU-Based Intrusion Detection Method

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IEEE ACCESS 2024年 12卷 135535-135550页

作者： Xia, Zhixin He, Siyuan Liu, Changwei Liu, Yongshan Yang, Xiaolei Bu, Huifeng Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China Qinhuangdao Port Co Ltd Branch Co 9 Qinhuangdao 066004 Peoples R China

Intrusion detection systems (IDS) identify network intrusions by detecting abnormal traffic data, thereby ensuring network security. However, intrusion detection data can vary with changes in the network and attack environment, resulting in poor performance and portability of intrusion detection algorithms. Therefore, an intrusion detection method based on PSO-GA hyperparameter optimized ResNet-BiGRU is proposed. The two-layer bidirectional gated recurrent unit (BiGRU) is connected to the fully connected layer of the residual neural network (ResNet). Firstly, ResNet is used to extract parallel local features, and BiGRU is used to extract long-distance-dependent features from the parallel local features, and the attention mechanism is added after the BIGRU to utilize correlation between the features to assign weights to the extracted features, so as to more comprehensively capture the important features of network intrusion and improve the detection performance. At the same time, the parameters of the basic particle swarm optimization (PSO) are dynamically optimized and combined with the genetic algorithm (GA) to perform a mutation operation when the iterative process falls into a local optimal solution, adding a random perturbation to the current velocity and position of the particles, so that the particles are able to explore new regions in the space in order to jump out of the local optimal solution, and ultimately achieve automatic optimization of the hyperparameters of the ResNet-BiGRU model to achieve a model with better generalization performance. Finally, the proposed method is validated by using the variant NSL-KDD dataset, which achieves an accuracy of 98.46% and average precision, average recall and average False Alarm Rate(FAR) of 91.84%, 95.99% and 0.31%, and achieved high accuracy on three datasets KDD99, UNSW-NB15, and CIC-IDS 2017. The method is proved to have a strong intrusion detection capability by comparison experiments with other algorithms.

关键词： Feature extraction Intrusion detection optimization Deep learning Accuracy Convolutional neural networks Adaptation models Bidirectional control particle swarm optimization Residual neural networks Bidirectional gated recurrent unit intrusion detection particle swarm optimization algorithm residual neural network

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Multi-Objective optimization Analysis of Electromagnetic Performance of Permanent Magnet Synchronous Motors Based on the PSO algorithm

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ENERGIES 2024年第18期17卷 4637页

作者： Cen, Yufei Shen, Haoyu Wang, Xiaoyuan Wu, Yongming Du, Jingjuan Changan Univ Sch Construct Machinery Xian 710064 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Leadr Technol Liuzhou Co Ltd Liuzhou 545000 Peoples R China Tianjin Chengjian Univ Sch Control & Mech Engn Tianjin 300384 Peoples R China

In order to optimize the electromagnetic performance of a permanent magnet synchronous motor (PMSM) during operation, this paper takes the size of the stator slot structure of the motor as the optimization variable and the peak cogging torque and no-load back electromotive force (EMF) amplitude of the motor as the optimization objectives. A multi-objective optimization method based on the particle swarm optimization (PSO) algorithm is adopted to obtain a structural parameter combination that minimizes the peak cogging torque and no-load back EMF amplitude while meeting the reasonable range requirements of magnetic flux density amplitude. The optimized motor structure design prototype is experimentally verified. The results show that through multi-objective optimization based on the PSO algorithm, the electromagnetic performance of the motor has been improved, with a reduction of 36.33% in peak cogging torque and 2.65% in peak no-load back EMF, indicating a reasonable magnetic flux density amplitude. The experimental results of the optimized prototype show that the difference between the theoretical simulation values and the experimental values is within a reasonable range, which verifies the effectiveness of the multi-objective optimization method.

关键词： permanent magnet synchronous motor particle swarm optimization algorithm multi-objective optimization electromagnetic performance

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Measuring moisture content of dead fine fuels based on the fusion of spectrum meteorological data

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Journal of Forestry Research 2023年第5期34卷 1333-1346页

作者： Bo Peng Jiawei Zhang Jian Xing Jiuqing Liu Mingbao Li Northeast Forestry University Harbin 150040People’s Republic of China

Dead fine fuel moisture content(DFFMC)is a key factor affecting the spread of forest fires,which plays an important role in evaluation of forest fire *** order to achieve high-precision real-time measurement of DFFMC,this study established a long short-term memory(LSTM)network based on particle swarm optimization(PSO)algorithm as a measurement model.A multi-point surface monitoring scheme combining near-infrared measurement method and meteorological measurement method is *** near-infrared spectral information of dead fine fuels and the meteorological factors in the region are processed by data fusion technology to construct a spectral-meteorological data *** surface fine dead fuel of Mongolian oak(Quercus mongolica *** Ledeb.),white birch(Betula platyphylla Suk.),larch(Larix gmelinii(Rupr.)Kuzen.),and Manchurian walnut(Juglans mandshurica Maxim.)in the maoershan experimental forest farm of the Northeast Forestry University were *** used the PSO-LSTM model for moisture content to compare the near-infrared spectroscopy,meteorological,and spectral meteorological fusion *** results show that the mean absolute error of the DFFMC of the four stands by spectral meteorological fusion method were 1.1%for Mongolian oak,1.3%for white birch,1.4%for larch,and 1.8%for Manchurian walnut,and these values were lower than those of the near-infrared method and the meteorological *** spectral meteorological fusion method provides a new way for high-precision measurement of moisture content of fine dead fuel.

关键词： Near infrared spectroscopy Meteorological factors Data fusion Long-term and short-term memory network particle swarm optimization algorithm

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A multiscale and multivariable differentiated learning for carbon price forecasting

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ENERGY ECONOMICS 2024年 131卷

作者： Chen, Linfei Zhao, Xuefeng Huazhong Univ Sci & Technol Sch Management Wuhan Peoples R China

Carbon price forecasting is important for policymakers and market participants. Due to the non -stationary and non -linearity of the carbon price, the commonly used methods adopt the ideology of 'decomposition and integration' to conduct multiscale forecasting. On this basis, multivariable forecasting discovers more informative knowledge with exogenous variables for carbon price forecasting, but it ignores that (i) the high -frequency and low -frequency components of the carbon price are mainly affected by different variables, and (ii) each variable contributes differently to each component forecasting. To address these challenges, we propose a multiscale and multivariable differentiated learning method for carbon price forecasting in this study. Specifically, different variables are introduced to forecast the high -frequency and low -frequency components, and a novel attentionweighted least squares support vector regression method is first proposed, in which the weight matrix of variables is constructed according to the idea of the attention mechanism. Furthermore, we analyze the contribution of each variable to the carbon price using Shapley additive explanations, thereby providing a reference for carbon market participants. We conduct experiments on the carbon price of the European Union Emissions Trading System and Hubei carbon market in China. Extensive results demonstrate that the proposed model achieves competitive and superior performance over the baseline and compared models.

关键词： Carbon price forecasting Variational mode decomposition particle swarm optimization algorithm Least squares support vector regression

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A comparative analysis of hybrid RF models for efficient lithology prediction in hard rock tunneling using TBM working parameters

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ACTA GEOPHYSICA 2024年第3期72卷 1847-1866页

作者： Zhou, Jian Yang, Peixi Yong, Weixun Khandelwal, Manoj Huang, Shuai Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Federat Univ Australia Inst Innovat Sci & Sustainabil Ballarat Vic 3350 Australia

With the escalating demand for underground mining and infrastructure construction, the optimization of tunnel construction has emerged as a primary concern for researchers. The geological conditions encountered during the excavation of hard rock tunnels using tunnel boring machines (TBM) significantly impact construction efficiency and cost-effectiveness. The existing lithology testing methods need to be more efficient in aligning with TBM operational efficiency. In recent years, the rapid advancement of artificial intelligence has paved the way for its integration into numerous domains, including tunnel engineering. To address this issue, this study proposes three innovative hybrid RF-based intelligent models, namely PSO-RF, ALO-RF, and GWO-RF, for the precise prediction of lithology in hard rock tunnels using TBM working parameters. The TBM operating parameters of the Jilin Yinsong Water Supply Project serve as the basis for this investigation. Twelve distinct characteristic parameters relevant to the lithology of the tunnel working face were carefully selected as input parameters for lithology prediction. Comparative analysis of the three hybrid models reveals that GWO-RF demonstrates exceptional lithology prediction performance (ACC = 0.999924;PREA = 0.0.9999976;RECA = 0.999775;F1A = 0.999876;Kappa = 0.999911), whereas PSO-RF and ALO-RF exhibit slightly inferior performance. Nonetheless, all three hybrid models exhibit a significant improvement in prediction accuracy compared to the unoptimized RF model. The research findings presented herein facilitate the swift determination of TBM working surface lithology, enabling timely adjustment of TBM working parameters, reducing equipment wear and tear, and enhancing construction efficiency.

关键词： Tunnel boring machine Lithology prediction Random forest particle swarm optimization algorithm Ant-lion optimization algorithm Gray wolf optimization algorithm

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Research on the Bi-level Programming Model for Ticket Fare Pricing of Urban Rail Transit based on particle swarm optimization algorithm

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Procedia - Social and Behavioral Sciences 2013年 96卷 633-642页

作者： Zhao Xueyu Yang Jiaqi Wuhan University of Technology School of Transportation Heping Avenue 1040 Wuchang District Wuhan City 430063

As a public service facility, the social and economic benefits of urban rail transit ticket fare are both important, so reasonable ticket fare is a key for the solid development of urban rail transit. The social and economic benefits should be taken into account under the competitive condition led by various modes of transportation in order to get an optimal strategy in ticket fare pricing of urban rail transit on the premise of meeting the service quality standard. Here, the factors considered in the ticket fares fare pricing of urban rail transit in the domestic and foreign cities are summarized, after which the Logit model of the mode split within the public transit system is established. With considering both the respective benefits of the urban rail transit company and the travellers, a bi-level programming model is established together with the solution idea to the model with the particle swarm optimization algorithm. The example demonstrates the feasibility and effectiveness of the bi-level programming model and the related measures and the particle swarm ooptimization aalgorithm is fittable for the urban rail transit fare pricing. The suggestions proposed from the result of the example are helpful for the decision making of ticket fare pricing of urban rail transit.

关键词： Ticket fare pricing Urban rail transit Logit model Bi-level programming model particle swarm optimization algorithm

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