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Parameter Identification of Battery Based on Improved bso algorithm

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2025年第3期20卷 1475-1483页

作者： Wu, Zhong-Qiang Shang, Meng-Yao Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China

Establishing ECM (equivalent circuit model) and identifying its parameters are very important to the SOC estimation of battery. A third-order Thevenin model of battery is established to improve the accuracy of ECM. In order to improve the performance of bso (Beetle Swarm Optimization) algorithm effectively, an improved bso algorithm, based on chaotic initialization and Gaussian perturbation, is proposed and used to identify the parameters of the battery model. Chaotic initialization has ergodicity properties, so it can increase the probability that the optimal value is found, and the estimation accuracy is improved. Gaussian perturbation is introduced for the velocity updating of the particle, it is beneficial to avoid the local optimal solution and accelerates the convergence speed. The optimization performance of the algorithm is tested by five test functions. The results show that: the improved algorithm has faster convergence speed and higher estimation precision compared with PSO and GA algorithm. The improved algorithm is used to identify the parameters of the battery model, and good convergence speed and high precision are achieved.

关键词： Battery Parameter identification bso algorithm Gaussian perturbation

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GPC parameter tuning of multivariable VAV air conditioning system based on population attenuation bso algorithm 41

GPC parameter tuning of multivariable VAV air conditioning s...

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第41届中国控制会议

作者： Ning He Junchen Li Mengrui Zhang School of Mechanical and Electrical Engineering Xi'an University of Architecture and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Aiming at the control difficulties of variable air volume(VAV) air conditioning system,such as large lag and multivariable coupling,a parameter tuning method of multivariable VAV air conditioning system generalized predictive controller(GPC) based on population attenuation beetle swarm optimization(bso) algorithm is ***,the weight parameters and learning factors of bso are dynamically selected,and the population attenuation function and event trigger(ET)conditions are reasonably selected to obtain better optimization ***,a novel GPC parameter tuning method based on population attenuation bso algorithm is proposed,which can improve the time-domain performance of the ***,simulation and hardware in the loop experiments are carried *** results show that the method which proposed by this paper is effective.

关键词： Generalized predictive control Variable air volume air conditioning system bso algorithm Controller parameters tuning

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Ship Course Control Based on bso-PID Online Self-Optimization algorithm 5

Ship Course Control Based on BSO-PID Online Self-Optimizatio...

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5th International Conference on Transportation Information and Safety (ICTIS)

作者： Wang, Le Liu, Jialun Wu, Qing Wang, Xuming Wuhan Univ Technol Sch Logist Engn Wuhan 430063 Peoples R China Wuhan Univ Technol Natl Engn Res Ctr Water Transport Safety Wuhan 430063 Peoples R China

ISBN: (纸本)9781728104898

Course control is one of the key research points for intelligent navigation of ships. Based on the optimization characteristics of Beetle Swarm Optimization (bso) algorithm, this paper designs a ship course online self-optimization controller. Firstly, the ship MMG model is established, and the rudder turning rate constraint is introduced to establish the course control objective function. Then, based on the PSO algorithm, the BAS algorithm is improved to the bso algorithm, and the bso-PID algorithm is used for online course control. Through a series of simulation experiments, it shows that the method of self-optimizing PID parameters by bso algorithm has better control effect on ship course control. Furthermore, it lays the foundation for further research in combination with other algorithms.

关键词： Course control BAS algorithm bso algorithm PID algorithm Online self-optimization

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Research on Weigh-in-Motion algorithm of Vehicles Based on bso-BP

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SENSORS 2022年第6期22卷 2109页

作者： Xu, Suan Chen, Xing Fu, Yaqiong Xu, Hongwei Hong, Kaixing China Jiliang Univ Sch Mech & Elect Engn Hangzhou 310018 Peoples R China

Weigh-in-motion (WIM) systems are used to measure the weight of moving vehicles. Aiming at the problem of low accuracy of the WIM system, this paper proposes a WIM model based on the beetle swarm optimization (bso) algorithm and the error back propagation (BP) neural network. Firstly, the structure and principle of the WIM system used in this paper are analyzed. Secondly, the WIM signal is denoised and reconstructed by wavelet transform. Then, a BP neural network model optimized by bso algorithm is established to process the WIM signal. Finally, the predictive ability of BP neural network models optimized by different algorithms are compared and conclusions are drawn. The experimental results show that the bso-BP WIM model has fast convergence speed, high accuracy, the relative error of the maximum gross weight is 1.41%, and the relative error of the maximum axle weight is 6.69%.

关键词： WIM bso algorithm wavelet transform BP neural network

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Multi-objective optimisation method of power grid partitioning for wide-area backup protection

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第3期12卷 696-703页

作者： Wang, Ziqi He, Jinghan Xu, Yin Crossley, Peter Zhang, Dahai Beijing Jiaotong Univ Power Syst Protect & Control Res Lab Shangyuancun 3 Beijing Peoples R China Univ Manchester Sch Elect & Elect Engn Manchester M13 9PL Lancs England

Rational partitioning method of power grid is the premise and foundation to implement wide-area backup protection with multi-source information. This study shows the universal structure of a wide-area backup protection system based on regional centralised decision-making mode, and presents a multi-objective optimisation method of power grid partitioning. Under the premise of reliable protection, a multi-objective function is built, comprehensively considering the communication performance, overlap rate, and the balance of regional workloads. To obtain the optimal result, the brain storm optimisation (bso) algorithm is used in this study. The method is applied on IEEE 11-bus and 39-bus test systems and simulated data is used to verify the effectiveness and feasibility of the optimisation method.

关键词： optimisation power system reliability power system protection IEEE 39-bus test systems IEEE 11-bus test systems bso algorithm brain storm optimisation algorithm regional workload balance overlap rate communication performance protection reliability regional centralised decision-making mode wide-area backup protection system universal structure multisource information rational partitioning method power grid partitioning multiobjective optimisation method

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Channel assignment based on bee algorithms in multi-hop cognitive radio networks

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IET COMMUNICATIONS 2014年第13期8卷 2356-2365页

作者： Ghasemi, Ahmad Masnadi-Shirazi, Mohammad Ali Biguesh, M. Qassemi, Foad Shiraz Univ Dept Elect & Comp Engn Shiraz Iran Kerman Grad Univ Technol Dept Civil Engn Kerman Iran

Spectrum management policies are responsible for poor utilisation of the radio spectrum. By carrying out dynamic spectrum management (DSM), cognitive radio (CR) can increase the radio spectrum in wireless systems efficiently. CR technology accounts for the improvement in the spectrum utilisation significantly. One issue of DSM in CR is the assignment of frequency channels among its users. Herein, a general model and four utility functions for optimal channel assignment in open spectrum systems such as CR networks have been defined. First, a new utility function with a better fairness than the other functions is proposed. Then, two new different channel assignment methods, based on the artificial bee colony (ABC) and bee swarm optimisation (bso) algorithms, are proposed, whereas other certain evolutionary algorithms and colour sensitive graph colouring (CSGC) are used to compare the performances. In order to decrease the search space, based on the channel availability and interference constraints a mapping process between the channel assignment matrix and the position of the bees has been proposed. Our simulation results, compared to the optimal solutions, show that our algorithms drastically improve network performance by reducing interference.

关键词： cognitive radio evolutionary computation matrix algebra channel allocation channel assignment matrix multihop cognitive radio networks spectrum management policies dynamic spectrum management DSM spectrum utilisation CR open spectrum systems artificial bee colony algorithm ABC algorithm bee swarm optimisation algorithm bso algorithm evolutionary algorithms colour sensitive graph colouring CSGC channel availability interference constraints

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Bees swarm optimization guided by data mining techniques for document information retrieval

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 94卷 126-136页

作者： Djenouri, Youcef Belhadi, Asma Belkebir, Riadh Ulsan Natl Inst Sci & Technol Ulsan South Korea USTHB RIMA Lab Algiers Algeria USTHB LRIA Lab Algiers Algeria

This paper explores advances in the data mining field to solve the fundamental Document Information Retrieval problem. In the proposed approach, useful knowledge is first discovered by using data mining techniques, then swarms use this knowledge to explore the whole space of documents intelligently. We have investigated two data mining techniques in the preprocessing step. The first one aims to split the collection of documents into similar clusters by using the k-means algorithm, while the second one extracts the most closed frequent terms on each cluster already created using the DCI_Closed algorithm. For the solving step, bso (Bees Swarm Optimization) is used to explore the cluster of documents deeply. The proposed approach has been evaluated on well-known collections such as CACM (Collection of ACM), TREC (Text REtrieval Conference), Webdocs, and Wikilinks, and it has been compared to state-of-the-art data mining, bio-inspired and other documents information retrieval based approaches. The results show that the proposed approach improves the quality of returned documents considerably, with a competitive computational time compared to state-of-the-art approaches. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Information retrieval Data mining Big data bso algorithm Bio-inspired methods

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Data Mining Based Integrated Electric-Gas Energy System Multi-Objective Optimization

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Energy Engineering 2022年第6期119卷 2607-2619页

作者： Zhukui Tan Yongjie Ren Hua Li Weili Ren Xichao Zhou Ming Zeng North China Electric Power University Beijing102206China State Grid Integrated Energy Service Group Beijing100052China

With the proposal of carbon neutrality,how to improve the proportion of clean energy in energy consumption and reduce carbon dioxide emissions has become the important challenge for the traditional energy *** on the idea of multi-energy complementarity,a typical integrated energy system consisting of electric system and gas system is constructed based on the application of power to gas(P2G)technology and gas turbine in this ***,a multi-objective optimization model with economic improvement,carbon emission reduction and peak-load shifting as objectives is proposed,and solved by bso ***,a typical power-gas coupling system is selected as an example to verify the effectiveness of the *** results showed that the proposed multi-objective optimization model based on bso algorithm can better play the complementary characteristics of the electric and gas system,and significantly improve the comprehensive benefits of system operation.

关键词： Integrated energy system bso algorithm power-gas coupling system clean energy consumption

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Automatically interactive group VIKOR decision making mechanism based on bso-SNA

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APPLIED SOFT COMPUTING 2021年第PartB期113卷 107979-107979页

作者： Tao, Xiwen Jiang, Wenqi Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Jiangsu Peoples R China

In order to improve the reliability of alternative ranking and group consensus adaptively, this paper develops an automatically interactive group decision-making (GDM) framework under the VIKOR environment. The proposed framework contains an interaction network mechanism based on social network analysis (SNA) and an opinion interaction mechanism based on Brain Strom Optimization (bso) algorithm. Firstly, to connect isolated decision-makers (DMs), interaction relationships among DMs are built based on the SNA and similarity measurement. Secondly, the bso algorithm is introduced to design the opinion interaction mechanism, including subgroup identification model and preference iteration model. The preference iteration model contains an adaptive preference update rule (UR), iteration stop rule (ISR), and solution selection rule (SSR) to enhance group consensus adaptively rather than force DMs to change the preference. Thirdly, the proposed interactive group VIKOR (GVIKOR) method is applied in SARS-cov-2 nucleic acid detection sites evaluation. Finally, consistency and comparison analysis are developed to illustrate the validity and robustness of evaluation results. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Interactive decision making Group consensus Group VIKOR bso algorithm

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Beetle swarm optimisation for solving investment portfolio problems

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JOURNAL OF ENGINEERING-JOE 2018年第16期2018卷 1600-1605页

作者： Chen, Tingting Zhu, Yongjian Teng, Jun Jiangsu Univ Fac Sci Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Nanchang Univ Inst Adv Study Nanchang 330031 Jiangxi Peoples R China

A portfolio model is established after analysing the investment environment of the artificial intelligence concept stocks in China. To reduce the risk of investment, the beetle swarm optimisation (bso) is proposed. bso, based on the beetle antennae search (BAS) and the standard particle swarm optimisation (PSO), is derived from the standard PSO but the update rules of each particle originate from BAS. In global searching, bso, making the model get a lower value at risk, is more capable than standard PSO, which is easily trapped in local optimal defects. This study tries to solve portfolio model by using bso algorithm. The results prove that bso can do better in dealing with optimisation problems of constrained multi-dimensional functions.

关键词： investment search problems particle swarm optimisation beetle swarm optimisation beetle antennae search BAS standard particle swarm optimisation standard PSO global searching portfolio model bso algorithm optimisation problems investment portfolio problems investment environment artificial intelligence concept stocks

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