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第34届中国控制与决策会议

作者： Wei He Hongyan Ma Yingda Zhang Shuai Wang Jiaming Dou Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education

Accurate state of charge(SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and prevent it from over-charging or ***,it is difficult to get an accurate value of SOC since it is an inner state of a battery cell,which cannot be directly *** order to improve the estimation accuracy of SOC,this paper develops a SOC estimation model for a lithium-ion battery using a particle swarm optimization-Extreme Learning Machine(PSO-ELM) *** PSO is applied to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal ELM *** inputs to the PSO-ELM model are the battery voltage,current,and temperature,and the output is the actual SOC *** performance of the proposed model is compared with BP neural network and ELM models and verified based on the mean square error(MSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and SOC *** results demonstrate that the PSO-ELM model offers higher accuracy and lower SOC error rate than ELM and BP neural network models.

关键词： Lithium-ion Battery State of Charge particle swarm optimization algorithm Extreme Learning Machine

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Application of Option Pricing Theory in Water Conservancy Finance

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JOURNAL OF COASTAL RESEARCH 2020年第Spe104期36卷 633-636页

作者： Lu, Xiaoli He, Guang Chongqing Technol & Business Univ Res Ctr Econ Upper Reaches Yangtze River Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Coll Math & Stat Chongqing 400067 Peoples R China

Financial support for water conservancy construction is an important approach to promote the development of water conservancy economy. In order to deal with numerical solution for stock option in water conservancy finance, a hybrid optimization algorithm is proposed. By virtue of the relation between Black-Scholes model and heat equation, a class of heat equations with initial-boundary values is established based on Schwarz waveform relaxation algorithm, meanwhile particle swarm optimization algorithm is applied to estimate parameters in option pricing model. In numerical experiments, the hybrid optimization algorithm is used to seek the approximate value of call option based on water concept stock, and it obtains better estimation results than existed methods.

关键词： Black-Scholes model water concept stock schwarz waveform relaxation algorithm heat equation particle swarm optimization algorithm

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Integration of particle swarm optimization and genetic algorithm for dynamic clustering

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INFORMATION SCIENCES 2012年 195卷 124-140页

作者： Kuo, R. J. Syu, Y. J. Chen, Zhen-Yao Tien, F. C. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei Taiwan Vanguard Int Semicond Corp Hsinchu Taiwan De Lin Inst Technol Dept Business Adm New Taipei City Taiwan Natl Taipei Univ Technol Dept Ind Engn & Management Taipei Taiwan

Although the algorithms for cluster analysis are continually improving, most clustering algorithms still need to set the number of clusters. Thus, this study proposes a novel dynamic clustering approach based on particle swarm optimization (PSO) and genetic algorithm (GA) (DCPG) algorithm. The proposed DCPG algorithm can automatically cluster data by examining the data without a pre-specified number of clusters. The computational results of four benchmark data sets indicate that the DCPG algorithm has better validity and stability than the dynamic clustering approach based on binary-PSO (DCPSO) and the dynamic clustering approach based on GA (DCGA) algorithms. Furthermore, the DCPG algorithm is applied to cluster the bills of material (BOM) for the Advantech Company in Taiwan. The clustering results can be used to categorize products which share the same materials into clusters. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Cluster analysis Dynamic clustering particle swarm optimization algorithm Genetic algorithm

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Thermo-economic optimization of the direct-condensation radiant heating panel: A numerical-based approach

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ENERGY AND BUILDINGS 2021年 240卷 110908-110908页

作者： Shao, Suola Zhang, Huan Liu, Zhikai You, Shijun Wang, Yaran Tianjin Univ Sch Environm Sci & Engn Haihe Educ Area Tianjin 300350 Peoples R China Tianjin Univ Ey Lab Efficient Utilizat Low & Medium Grade Ener Minist Educ China Tianjin 300350 Peoples R China

The direct-condensation radiant heating panel (DRHP) is considered as an efficient heating terminal in space heating. In this study, a numerical-based optimization approach is proposed for the thermoeconomic performance improvement of the DRHP. An improved numerical model of the DRHP considering the heat conduction of the composite straight-and-circular fins and that of the connecting segments is established. The analytical solutions of the heat conduction of the composite straight-and-circular fins are derived to improve the prediction accuracy and computation speed. The model is validated by the experimental data. Based on the proposed model, the particle swarm optimization (PSO) algorithm is adopted to maximize the heating capacity under per unit cost of the DRHP. The optimization constraints are determined with the parametric analysis and the iterations are examined to be 30. Based on the optimization approach, the optimized DRHPs are obtained for heat pump units with different output powers, and the heating capacity under per unit cost of the optimized DRHP is increased by 44.2%. The proposed optimization approach is appropriate for the optimization of DRHP. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Direct-condensation radiant heating panel Improved mathematical model Parametric analysis particle swarm optimization algorithm Thermo-economic optimization

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Expeditious COVID-19 similarity measure tool based on consolidated SCA algorithm with mutation and opposition operators

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APPLIED SOFT COMPUTING 2021年 104卷 107197-107197页

作者： Issa, Mohamed Zagazig Univ Fac Engn Comp & Syst Dept Zagazig Egypt Nahda Univ Fac Comp & Informat Bani Suwayf Egypt

COVID-19 is a global pandemic that aroused the interest of scientists to prevent it and design a drug for it. Nowadays, presenting intelligent biological data analysis tools at a low cost is important to analyze the biological structure of COVID-19. The global alignment algorithm is one of the important bioinformatics tools that measure the most accurate similarity between a pair of biological sequences. The huge time consumption of the standard global alignment algorithm is its main limitation especially for sequences with huge lengths. This work proposed a fast global alignment tool (G-Aligner) based on meta-heuristic algorithms that estimate similarity measurements near the exact ones at a reasonable time with low cost. The huge length of sequences leads G-Aligner based on standard Sine-Cosine optimization algorithm (SCA) to trap in local minima. Therefore, an improved version of SCA was presented in this work that is based on integration with PSO. Besides, mutation and opposition operators are applied to enhance the exploration capability and avoiding trapping in local minima. The performance of the improved SCA algorithm (SP-MO) was evaluated on a set of IEEE CEC functions. Besides, G-Aligner based on the SP-MO algorithm was tested to measure the similarity of real biological sequence. It was used also to measure the similarity of the COVID-19 virus with the other 13 viruses to validate its performance. The tests concluded that the SP-MO algorithm has superiority over the relevant studies in the literature and produce the highest average similarity measurements 75% of the exact one. (C) 2021 Elsevier B.V. All rights reserved.

关键词： COVID-19 Bioinformatics Pairwise global alignment Sine-Cosine optimization algorithm particle swarm optimization algorithm

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Peak shaving and heat supply flexibility of thermal power plants

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APPLIED THERMAL ENGINEERING 2021年 193卷 117030-117030页

作者： Wang, Congyu Song, Jiwei Zhu, Lingkai Zheng, Wei Liu, Zhaozhao Lin, Chengkun Shandong Univ Inst Adv Technol 17923 Jingshi Rd Jinan 250061 Peoples R China Shandong Univ Inst Thermal Sci & Technol 17923 Jingshi Rd Jinan 250061 Peoples R China State Grid Corp China Shandong Elect Power Res In 2000 Wangyue Rd Jinan 250003 Peoples R China

The operational flexibility of thermal power plants is important to consume renewable energy generation, especially in the regions where combined heat and power (CHP) units account for a high proportion. Focusing on the relationship between peak-shaving capacity of CHP units and the consumption of renewable energy generation, the problem about operational flexibility of CHP plants is analyzed in this paper. From the perspective of entire CHP plants rather than the renovation of one CHP unit, the problem regarding the operation scheduling without cost or risk is addressed. A plant-level operation domain model is established, which can improve the operational flexibility of CHP plants. The heat and power adjustable ranges of CHP plants were obtained by particle swarm optimization. Also the CHP plant located in northern China was taken as an example. The power load downward adjustment range of four periods in the heating season are 9.21%, 20.44%, 14.09% and 5.31% respectively, compared with the entire plant actual power under the same heat load. Moreover, the operational flexibility of CHP plants can be enlarged by decreasing the pressure of heating extraction. Compared with the entire plant actual operating conditions, the consumption of renewable energy generation could be increased by 268.56 million kWh approximately and about 0.22 million tons CO2 emissions can be reduced per heating season if the reference plant operates with minimum power load constantly.

关键词： CHP Operation domain Flexibility Renewable energy generation particle swarm optimization algorithm

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Recovery trajectory planning for the reusable launch vehicle

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AEROSPACE SCIENCE AND TECHNOLOGY 2021年 117卷 106965-106965页

作者： Cheng, Guanghui Jing, Wuxing Gao, Changsheng Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China

Trajectory planning method for the entire recovery process of the reusable launch vehicle is studied. Firstly, a revised trajectory correction method (RTCM) is proposed to reduce the maximum normal aerodynamic load in the endo-atmosphere unpowered descent phase and improve the vehicle's landing accuracy on the influence of wind field. It consists of two parts, namely moving the ignition point of the vertical landing phase on the horizontal plane and introducing preset steady wind field into trajectory planning. Secondly, a compound trajectory planning method, which combines particle swarm optimization algorithm (PSO) with Polynomial Guidance Law (PGL), is proposed to complete the optimization task of the entire recovery process. Employing PSO algorithm can avoid the complex calculation of additional angle of attack caused by the preset steady wind field in traditional trajectory correction. To overcome the premature convergence of basic PSO, the traditional re-initialization mechanism is improved. Finally, the guidance simulation of two phases greatly affected by wind is accomplished. The effectiveness of the proposed methods is demonstrated with some scenarios and Monte Carlo simulation. (C) 2021 Elsevier Masson SAS. All rights reserved.

关键词： Reusable launch vehicle Trajectory correction Compound trajectory planning method particle swarm optimization algorithm Polynomial guidance law

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Research on Timetabling Problems Based on particle swarm optimization algorithm

Research on Timetabling Problems Based on Particle Swarm Opt...

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2012 3rd International Conference on Information Technology for Manufacturing Systems(ITMS 2012)

作者： Xinmin Ma Linli Wu Basic Courses Department Bengbu Automobile Non-Commissioned Officer Academy NO 4 Department Bengbu Navy Petty Officer Academy

A new algorithm for timetabling based on particle swarm optimization algorithm was proposed, and the key problems such as particle coding, fitness function fabricating, particle swarm initialization and crossover operation were settled. The fitness value declines when the evolution generation increases. The results showed that it was a good solution for course timetabling problem in the educational system.

关键词： particle swarm optimization algorithm timetabling fitness function

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Dynamic space reduction optimization framework and its application in hull form optimization

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APPLIED OCEAN RESEARCH 2021年 114卷 1页

作者： Zheng, Qiang Feng, Bai-Wei Chang, Hai-Chao Liu, Zu-Yuan Wuhan Univ Technol Key Lab High Performance Ship Technol Minist Educ Wuhan Peoples R China Wuhan Univ Technol Sch Transportat Wuhan Peoples R China

Hull form optimization is a typical complex engineering problem. The complex design performance space often results in a low optimization efficiency as an optimal solution cannot be ensured. Presently, several methods, such as efficient optimization algorithms, approximate model technology, and high-performance computing are primarily used to reduce the calculation time. However, these methods cannot satisfy practical application requirements in terms of efficiency and solution accuracy. Thus, we investigated a dynamic space reduction optimization framework (DSROF), in this study, wherein data mining is continuously performed during the optimization process to dynamically reduce the range and number of variables. DSROF enables subsequent optimization only in the range that exhibits a high performance, thereby reducing redundant calculations, improving optimization efficiency, and ensuring a higher degree of accuracy. Furthermore, we applied DSROF to function examples and hull form optimization. The results indicate that the use of the DSROF can reduce the calculation cost in hull form optimization by 23% in comparison with that of the particle swarm optimization algorithm.

关键词： Hull form optimization Data mining Dynamic space reduction particle swarm optimization algorithm

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A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM

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ENERGY 2021年 234卷 121236-121236页

作者： Ren, Xiaoqing Liu, Shulin Yu, Xiaodong Dong, Xia Qilu Univ Technol Shandong Acad Sci Dept Elect Engn & Automat Jinan 250353 Peoples R China

State-of-charge (SOC) estimation of lithium-ion battery is one of the core functions of battery management system. In order to improve the estimation accuracy of SOC, this paper proposes a long shortterm memory neural network based on particle swarm optimization (PSO-LSTM). Firstly, the key parameters of LSTM are optimized by PSO algorithm, so that the data characteristics of lithium-ion battery can match the network topology. In addition, random noise is added to the input layer of PSO-LSTM neural network to improve the anti-interference ability of the network. Finally, experiments show that the proposed method can achieve accurate estimation under different conditions. The estimates based on PSO-LSTM converge to the real state-of-charge within an error of 0.5%. (c) 2021 Published by Elsevier Ltd.

关键词： Lithium-ion battery SOC estimation particle swarm optimization algorithm Long short-term memory neural network

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