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A hybrid SVR-PSO model to predict concentration of sediment in typical and debris floods

EARTH SCIENCE INFORMATICS 2021年第1期14卷 365-376页

作者： Kazemi, Mahsa Sheikh Banihabib, Mohammad Ebrarim Soltani, Jaber Univ Tehran Coll Aburaihan Dept Water Engn Tehran Iran

Since sediment concentration is an effective factor on increasing debris flood's peak flow and damages from floods, developing new models to predict the sediment concentration of debris floods has crucial importance. In this study, a hybrid SVR-PSO model was proposed to predict the concentration of sediment in typical and debris floods, and it was examined in three basins located in Gilan, Mazandaran, and Tehran Provinces, Iran. Mean elevation and slope of the basin, the area of the basin, current day's rainfall, the rainfall of previous days (1-3 days before flood) for all rain-gauge stations of the basins, as well as the discharge of the previous day, were used as the input variables of the model. Then, various combinations of variables were tested to assess the factors influencing the concentration of sediment in typical and debris floods in order to find the best variable combination with a high performance in predicting the concentration of sediment in the studied floods. The results showed that basin elevation, current day's rainfall, previous day's discharge, rainfall of the previous day, basin area, rainfall of the previous two days, basin slope, and rainfall of the previous three days were the key factors influencing the concentration of sediment in typical and debris floods, respectively. Coefficient of determination, root mean square error, and mean absolute percentage error were estimated 0.96, 0.003, and 14.38% for the proposed model at the testing phase, respectively. This implies model's good performance for predicting the concentration of sediment in typical and debris floods so that the present model can provide reliable predictions of flood character in basins.

关键词： Concentration of sediment Debris flood particle swarm optimization algorithm Prediction Support vector regression

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Assessment of MV XLPE cable aging state based on PSO-XGBoost algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2023年 221卷

作者： Pan, Qiaosheng Zhang, Chi Wei, Xinyuan Wan, Aode Wei, Zeping Hefei Univ Technol Sch Instrument Sci & Optoelect Engn Hefei 230009 Anhui Peoples R China Sichuan Changhong Elect Co Ltd Mianyang 621000 Sichuan Peoples R China Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243032 Peoples R China

Cable aging is one of the main security risks to power systems. With the widely used cables in power systems, the accurate assessment of cable aging status is increasingly important. This study proposes an efficient assessment model based on the PSO-XGBoost algorithm, which integrates the particle swarm optimization (PSO) algorithm and the extreme gradient boosting (XGBoost) algorithm. The XGBoost model is established to assess the cable aging status with the inputs of partial discharge, operating life, corrosion condition and load condition. The PSO algorithm automatically optimizes parameters during XGBoost model training. Then, the standard performance evaluation metrics of the proposed assessment model are compared with four advanced classification models. The accuracy, precision, recall and F1-score of the assessment model are above 98%, indicating that the proposed PSO-XGBoost model can accurately assess the cable aging state. Furthermore, these calculation results of the proposed model are better than the other four benchmark models, which shows that the proposed model performs better in cable aging status assessment than the existing models.

关键词： Cable aging status assessment Extreme gradient boosting algorithm particle swarm optimization algorithm

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A simulation-based optimization for deploying multiple kinds road rescue vehicles in urban road networks

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年 181卷

作者： Tan, Zheyi Zhang, Qian Deng, Weiliang Zhen, Lu Shao, Wei Shanghai Univ Sch Management Shang Da Rd 99 Shanghai 200444 Peoples R China Shanghai Univ Sci Res Management Dept Shang Da Rd 99 Shanghai 200444 Peoples R China

In the rescue process of urban road traffic accidents, the decision-making support system is of great importance since it will affect the rescue response time, and the response time is used as an evaluation index of the system performance. A shorter response time can ensure that casualties are rescued timely and can help restore the road as soon as possible. This paper provides a decision tool for traffic accident rescue vehicles' integrated deployment problem in urban areas. In order to better simulate the stochastic rescue requests, the time and space distribution of traffic, and the dynamic rescue process, a simulation optimization model is established. This model determines the optimal deployment plan for three kinds of road rescue vehicles, i.e., ambulances, wreckers, and sweepers, in several time intervals in one day. To find a better solution, we incorporate a particle swarm optimization (PSO) method into the simulation and optimization procedures. The proposed method is validated by a case study in Shanghai. Additionally, sensitivity analysis is included to study the effects of various parameters and to confirm effectiveness and efficiency of the proposed method. The numerical experiments indicate that simply increasing the number of road rescue resources does not always improve the efficiency of road rescue.

关键词： Traffic accident rescue Vehicle deployment Simulation optimization particle swarm optimization algorithm

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Design and optimization of Personalized Education System Based on Intelligent algorithms

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Procedia Computer Science 2024年 243卷 514-522页

作者： Jie Zhu Yahong Liao Danni Lu Wuhan Railway Vocational College of Technology Wuhan 430205 Hubei China Xiamen Jin Shang Middle School Xiamen 361000 Fujian China Hubei Vocational College of Railway Transportation Wuhan 430070 Hubei China

The education industry, as the top priority of social operation, is constantly emerging with education systems or online education platforms based on internet technology. However, most of them are facing problems of rigidity, stiff, or resource scarcity. Therefore, this article aimed to establish a personalized education system to solve this problem and optimize the system based on intelligent algorithms. At the end of this article, a comparison was made on the algorithm performance of the decision tree algorithm in the intelligent algorithm. Compared with the original algorithm of the system, the accuracy increased from 70.35% to 75.68%. The system based on the intelligent algorithm also helped the students in the experimental class improve their grades, and even cleared the score record below 40 points, helping to improve the overall performance of the entire class.

关键词： Intelligent algorithm Decision Tree algorithm Personalized Education System particle swarm optimization algorithm

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An effective soft computing technology based on belief-rule-base and particle swarm optimization for tipping paper permeability measurement

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第3期10卷 841-850页

作者： Qian, Bin Wang, Qian-Qian Hu, Rong Zhou, Zhi-Jie Yu, Chuan-Qiang Zhou, Zhi-Guo Kunming Univ Sci & Technol Coll Informat Engn & Automat Dept Automat Kunming 650500 Yunnan Peoples R China Rocket Force Univ Engn Xian 710025 Shanxi Peoples R China High Tech Inst Xian Xian 710025 Shanxi Peoples R China Univ Texas Southwestern Med Ctr Dallas Dept Radiat Oncol Dallas TX 75235 USA

This paper proposes a soft computing technology based on belief rule base (BRB) system for the tipping paper permeability measurement in tobacco factory. In current studies about BRB, both the referential values of the antecedent attributes and the utilities of the consequents are given in advance and are not trained by using the dedicated optimal algorithms. The limitations of expert knowledge may lead to error of BRB because both the referential values and the utilities make a real difference on the structure of BRB, and the appropriate structure is helpful for tuning parameters more accurately. Therefore, this paper focuses on the structure and parameters optimization of BRB (SPO-BRB) by taking the referential values of the antecedent attributes and the utilities of the consequents into account to improve the input-output modeling ability of BRB. However, SPO-BRB is a nonlinear nonconvex optimization problem (NNOP). To deal with the NNOP of SPO-BRB, a particle swarm optimization algorithm with improved velocity update way and repair methods (PSO_VR) is proposed. A case study based on the data collected from a tobacco factory of china is carried out. The test results demonstrate the functionality of SPO-BRB and the effectiveness of PSO_VR.

关键词： Soft computing technology Belief rule base Parameter and structure identification particle swarm optimization algorithm Porosity measuring

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General metaheuristic-based methodology for computation and decomposition of LMPs

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ELECTRICAL ENGINEERING 2021年第2期103卷 793-811页

作者： Saraiva, Felipe Oliveira Silva Paucar, Vicente Leonardo Fed Univ Maranhao UFMA Dept Elect Engn BR-65085580 Sao Luis MA Brazil

Locational marginal prices (LMPs) are economic signals paramount to the deliberations, policy designs, and planning judgments of most electricity markets. This paper presents a general metaheuristic-based methodology applied in the scope of two subproblems to compute and decompose the LMPs considering the incorporation of a distributed slack bus model in the power flow formulation. The deterministic core of the methodology is governed by a process of sequential setting of maximum number of metaheuristic iterations based on load disharmony indices (LDIs) in the context of the initial subproblem. The metaheuristic crux of the methodology is anchored in a random initialization strategy based on time-traveling parameters in the environment of both subproblems. In the introductory arrangement of the optimization sequence, the chain order of the operating horizon periods is defined based on the LDIs. Due to its notorious merits and the breadth of its applications in power system problems, a particle swarm optimization (PSO) algorithm model is used to solve the mentioned subproblems. For the practical purposes of this paper, in the PSO algorithm model instance the particle coordinates are adopted as time-traveling metaheuristic parameters. Numerical simulations on a 3-bus system and on the IEEE 30-bus test system corroborate the efficiency and adequacy of the proposed methodology.

关键词： Electricity market LMP decomposition Load disharmony index Locational marginal price particle swarm optimization algorithm

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Constrained Multi-objective particle swarm optimization algorithm

Constrained Multi-objective Particle Swarm Optimization Algo...

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8th International Conference on Intelligent Computing (ICIC)

作者： Gao, Yue-lin Qu, Min N Ethn Univ Inst Informat & Syst Sci Yinchuan Ningxia Peoples R China

ISBN: (纸本)9783642318368

A particle swarm optimization for solving constrained multi-objective optimization problem was proposed (CMPSO). In this paper, the main idea is the use of penalty function to handle the constraints. CMPSO employs particle swarm optimization algorithm and Pareto neighborhood crossover operation to generate new population. Numerical experiments are compared with NSGA-II and MOPSO on three benchmark problems. The numerical results show the effectiveness of the proposed CMPSO algorithm.

关键词： particle swarm optimization algorithm constrained multi-objective optimization Pareto neighborhood crossover operation penalty function method

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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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particle swarm optimization-Based Deep Neural Network for Digital Modulation Recognition

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IEEE ACCESS 2019年 7卷 104591-104600页

作者： Shi, Wenzhe Liu, Dejun Cheng, Xing Li, Yang Zhao, Yang China Univ Petr Coll Informat Sci & Engn Beijing 102249 Peoples R China

Modulation recognition is a major task in many wireless communication systems including cognitive radio and signal reconnaissance. The diversification of modulation schemes and the increased complexity of the channel environment put higher requirements on the correct identification of modulated signals. Deep learning (DL) is considered as a potential solution to solve these problems due to the superior big data processing and classification capabilities. This paper proposes an efficient digital modulation recognition method based on deep neural network (DNN) model. Furthermore, we present the particle swarm optimization (PSO) algorithm to optimize the number of hidden layer nodes of the DNN so as to solve the problem that the traditional DNN is trapped in local minimum values and the number of hidden layer nodes needs selecting manually. In this paper, we utilize the proposed PSO-DNN method to learn characteristics extracted from the modulated signal added by additive white Gaussian noise (AWGN) and to train the network, which can improve the performance of recognition under the condition of low signal-to-noise ratio (SNR). The experimental results demonstrate that the recognition rate on this algorithm has improved by 9.4% and 8.8% compared with methods that adopt conventional DNN and support vector machine (SVM) when SNR equals 0 and 1 dB, respectively. Besides, another experiment compared with the genetic algorithm (GA) also proves that our proposed algorithm is more effective in optimizing the DNN. The proposed method is easy to be implemented so that it has a broad development prospect in modulation recognition.

关键词： Additive white Gaussian noise deep neural network digital modulation recognition particle swarm optimization algorithm

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Techno-economic Unified OPF Modeling for VSC-HVDC Converter Installation

ELECTRICA

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ELECTRICA 2021年第3期21卷 352-+页

作者： Agrawal, Sunilkumar Kundu, Prasanta SV Natl Inst Technol Dept Elect Engn Surat India

This paper demonstrates the analysis of novel methodology developed to select optimal buses for installation of convertor stations in the hybrid voltage source converter based high voltage direct current system. Here, a modified unified optimal power flow model is developed for the optimal power flow problem and solved using the particle swarm optimization technique for the voltage source converter based high voltage direct current network. The analysis has been performed for optimizing the various techno-economic objective functions, including generation cost, voltage deviation, and total power system losses, for better power system operation. The developed unified optimal power flow model's effectiveness and methodology for deciding the high voltage direct current converter's optimal location are examined, with several tests performed with modified five bus and IEEE-30 bus system. The impact of high voltage direct current line replacement is decided based on optimal results obtained for selected techno-economic objective functions by replacing each AC line with high voltage direct current independently. The obtained results have proved the voltage source converter high voltage direct current controller's impact on optimization of generation cost, voltage deviation, and total power system losses.

关键词： Optimal power flow particle swarm optimization algorithm power system optimization unified optimal power flow modeling voltage source converter based high voltage direct current

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