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APPLICATION OF PSO-ACO algorithm IN PATH PLANNING OF INTELLIGENT ROBOTS

MECHATRONIC SYSTEMS AND CONTROL 2025年第1期53卷 42-53页

作者： Wang, Xiaohan Miao, Yanzi Jiuzhou Polytech Sch Electromech & Automot Engn Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

As robot technology continues to develop and innovate, it is widely applied in various fields of production and life. Path planning (PP) is the cornerstone of autonomous navigation of intelligent robots (IRs). However, the current particle swarm optimisation (PSO) and ant colony optimisation (ACO) algorithms still have problems, such as slow convergence rate (CR), complexity, and large amount of computation. Therefore, research will improve the hybrid ACO algorithm and PSO algorithm to obtain feasible robot PP. Then, ACO algorithm is used to obtain the optimal solution (OS), and the particle swarm ant colony fusion algorithm is obtained. Compared with PSO and ACO algorithms, the shortest path of the fusion algorithm is 43.78 m, which is closer to the optimal path. In an environment with an obstacle ratio of 0.4, the optimal performance index of the fusion algorithm is 8.84%, and the number of iterations during convergence is 24. Compared with genetic algorithm (GA) and sampling based PP algorithm, CR of this fusion algorithm is faster and the average value of the optimal path is smaller when the obstacle ratio is 0.7. In summary, the fusion algorithm proposed in this research is effective in IR path planning (IRPP). This can improve the path optimisation ability of IRs and provide a basis for exploring more effective global dynamic PP methods in the future.

关键词： Path planning particle swarm optimisation algorithm ant colony optimisation intelligent robot fusion algorithm

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Data classification mining of university mental health education resources based on global search algorithm

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2023年第4期73卷 278-287页

作者： Fang, Jing Wang, Daoxun Henan Polytech Marxism Coll Zhengzhou Henan Peoples R China Huanghe Sci & Technol Univ Vocat & Tech Coll Zhengzhou Henan Peoples R China

To solve the problems of low accuracy and recall rate, as well as long classification mining time in traditional methods, a university mental health education resource data classification mining method based on global search algorithm is proposed. Collect data on university mental health education resources, identify abnormal data using isolated forests and perform correction processing. Extract resource data features using Fisher discriminant criteria and select data features. Build a data classification mining model for university mental health education resources, and use the particle swarm optimisation algorithm in the global search algorithm to construct an optimisation objective function for classification mining. Input the data to be processed into the optimised model to obtain relevant classification mining results. The experimental results show that the proposed method has a mean classification mining accuracy of 98.1%, a mean recall rate of 97.3% and a classification mining time of less than 1.28 s.

关键词： global search algorithm university mental health education resource classification mining isolation forests fisher's discriminant criterion particle swarm optimisation algorithm

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Unsupervised clustering algorithm for databases based on density peak optimisation

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2023年第3期16卷 313-326页

作者： Pu, Xiaochuan Seo, Wonchul Ruan, Qingqiang Zunyi Normal Univ Coll Informat Engn Guizhou 563002 Zunyi Peoples R China Pukyong Natl Univ Grad Sch Management Technol Busan 48513 South Korea

In order to improve the precision and sensitivity of traditional unsupervised clustering algorithms, an unsupervised clustering algorithm based on density peak optimisation is proposed. K-nearest neighbour is used to set a new method to measure the sample density and sample distance. The selected sample is the initial cluster centre, and the number of clusters is automatically determined. The improved K-means algorithm and particle swarm optimisation algorithm are introduced to optimise the convergence process of the algorithm. Experimental results show that compared with the traditional algorithm, the clustering accuracy of the proposed algorithm can be stable at 95-100%, and the sensitivity of the algorithm is also relatively ideal. With the increase in the number of data genes, the sensitivity is always above 95%. The running time is about 0.2 min, and the data show that the proposed algorithm meets the requirements of the current application field.

关键词： density peak optimisation database unsupervised clustering initial cluster centre K-means algorithm particle swarm optimisation algorithm

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Short-term forecasting method for lighting energy consumption of large buildings based on time series analysis

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2023年第3期45卷 220-232页

作者： Li, Yanpeng Zhang, Guofeng Univ Sci & Technol Liaoning Dept Environm Design Anshan Liaoning Peoples R China Univ Sci & Technol Liaoning Dept Architecture Anshan Liaoning Peoples R China

In order to overcome the problems of high data noise, low prediction accuracy and long prediction time in the traditional short-term prediction method of lighting energy consumption of large buildings, a short-term prediction method of lighting energy consumption of large buildings based on time series analysis is proposed in this paper. The improved threshold function is used to denoise the data, and the fuzzy c-means clustering algorithm is used to cluster the denoised data. The time series analysis method is used to construct the self-excitation threshold autoregressive model. When the model parameters are optimal, the clustered data are input into the model to output the short-term prediction results of lighting energy consumption of large buildings. The experimental results show that compared with the traditional method, the average data noise of this method is 12.3 dB, the prediction accuracy remains above 94% and the average prediction time is only 57 ms.

关键词： time series analysis large buildings lighting energy consumption short-term forecast fuzzy c-means clustering algorithm self-excitation threshold autoregressive model particle swarm optimisation algorithm

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Offshore wind power prediction based on chaotic optimisation PSO-SCN-LSTM model

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2025年第1期21卷 48-63页

作者： Li, Xu Kou, Lei Zhang, Benfa Wang, Zhen Wen, Jingya Zhang, Fangfang Du, Jinyan Zhou, Yan Ke, Wende Qilu Univ Technol Inst Oceanog Instrumentat Shandong Acad Sci Qingdao 266075 Peoples R China State Grid Songyuan Power Supply Co Songyuan 138099 Peoples R China Southern Univ Sci & Technol Dept Mech & Energy Engn Shenzhen 518055 Peoples R China

Offshore wind power, as a clean energy source, is receiving increasing attention worldwide. To enhance the economic and safety performance of offshore wind power, short-term forecasting of wind power is essential. This paper proposes a model based on chaos optimisation integrated with particle swarm optimisation (PSO), stochastic configuration network (SCN), and long short-term memory (LSTM) algorithm. Firstly, leveraging the randomness and ergodicity of the complex logistic chaos system, the collected power data from wind turbines is utilised as the input data source for the PSO, enhancing the randomness of the data. Subsequently, the SCN is employed to optimise the PSO, increasing the variation in the hidden layer during iterations and mitigating the PSO's tendency to fall into local optima, thereby obtaining initial prediction values. Finally, the mechanism model of the LSTM is utilised for secondary prediction, further improving prediction accuracy. Compared with traditional algorithms, the optimised algorithm significantly reduces errors and enhances prediction precision.

关键词： chaos theory particle swarm optimisation algorithm random configuration network LSTM prediction model

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IMPROVED particle swarm algorithm FOR COOPERATIVE MULTI-TASK ALLOCATION OF HETEROGENEOUS UAVs

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MECHATRONIC SYSTEMS AND CONTROL 2023年第1期51卷 42-52页

作者： Lu, Qilin Chen, Yu Qi, Xiaogang Liu, Lifang Xidian Univ Sch Math & Stat Xian 710071 Shaanxi Peoples R China Sci & Technol Near Surface Detect Lab Wuxi Jiangsu Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China

This paper proposes a discrete particle swarm optimisation (DPSO) algorithm for solving the heterogeneous unmanned aerial vehicle (UAV) task allocation problem. Such an algorithm takes task priority, resource constraints flight distance, and task revenue into account. First, the specific particle is designed according to the characteristics of the problem, and the corresponding relationship between the allocation plans and the particles is given. A modified strategy is presented for the infeasible particles. On this basis, the original particle swarm algorithm was transformed to a DPSO algorithm. Then, in order to improve the local search ability of particles, an elite operator is introduced on the basis of DPSO, and local search is initiated with a certain probability, forming a new search strategy (IDPSO). Simulation results show that DPSO can be reasonable in solving heterogeneous UAV multi-task problems when the problem size is small. The optimal solution obtained by the proposed IDPSO algorithm is better than the DPSO algorithm, and as the scale of the task allocation problem increases, the superiority of the IDPSO algorithm becomes more significant.

关键词： Heterogeneous unmanned aerial vehicle cooperative multi-task allocation particle swarm optimisation algorithm

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Research on the Price Prediction Model of the Used New Energy Vehicles Based on PCA-PSO-BP Neural Network 5

Research on the Price Prediction Model of the Used New Energ...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Liu, Bowen Wu, Zhongcheng Anhui Univ Inst Phys Sci & Informat Technol Hefei 230031 Peoples R China Chinese Acad Sci High Magnet Field Lab HFIPS Hefei 230031 Peoples R China

ISBN: (纸本)9798350386783;9798350386776

A BP neural network evaluation model based on principal component analysis and particle swarm optimisation algorithm (PCA- PSO-BP) is proposed to address the problems of single selection indicators, excessive reliance on manual detection, and large evaluation error in the existing residual value evaluation of used new energy vehicles. Firstly, the principal component analysis method is used to screen out the most important feature indicators that affect the residual value of new energy vehicles. Then, the particle swarm optimization algorithm is used to optimize the weights and thresholds of the BP neural network. Finally, the selected features are input into the optimized BP neural network to output the residual value evaluation result. The results indicate that the PCA-PSO-BP neural network residual value evaluation model has high prediction accuracy and can provide reference for the current state of the art. The results indicate that the PCA-PSO-BP neural network residual value evaluation model has high prediction accuracy and can provide reference for the current residual value evaluation methods of second-hand new energy vehicles.

关键词： new energy vehicles residual value evaluation particle swarm optimisation algorithm bp neural network

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Adaptive backstepping controller design on Buck converter with a novel improved identification method

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IET CONTROL THEORY AND APPLICATIONS 2022年第5期16卷 485-495页

作者： Saadat, Seyyed Amirhossein Ghamari, Seyyed Morteza Mollaee, Hasan Shahrood Univ Technol Fac Elect & Robot Engn Shahrood Semnan Iran

An adaptive backstepping method is presented by this paper for a DC-DC Buck converter utilising a strategy for system identification with pulse width modulation in the presence of parametric uncertainties, load variations, and high variance noises. In this control structure, the system is assumed as a black-box block that can decrease the computational burden providing faster dynamics. An adaptive mechanism is adopted for the BSM using the Lyapunov definition, providing robust dynamics for the controller against various disturbances. In addition, a novel improved exponential recursive least-squares identification algorithm is proposed, which shows higher robustness in parametric estimations and can decrease the negative impact of disrupting factors on the estimator. Moreover, a particle swarm optimisation algorithm-based PID controller is designed to be compared with the proposed controller. Finally, the merits of the presented controller are validated for various working conditions through simulations and experiments. It can be seen that the adaptive backstepping method with the improved identification technique provides much better results with faster dynamics.

关键词： system identification particle swarm optimisation algorithm high variance noises identification method least-squares identification algorithm robust dynamics parametric uncertainties PWM invertors robust control three-term control control structure adaptive backstepping controller design load variations power generation control particle swarm optimisation adaptive control PID controller improved identification technique adaptive mechanism DC-DC buck converter adaptive backstepping method Lyapunov methods control system synthesis DC-DC power convertors black-box block pulse width modulation

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State-of-charge estimation for Lithium-Ion batteries using Kalman filters based on fractional-order models

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CONNECTION SCIENCE 2022年第1期34卷 162-184页

作者： Xing, Likun Ling, Liuyi Gong, Bing Zhang, Menglong Anhui Univ Sci & Technol Sch Elect & Informat Technol Huainan 232001 Peoples R China Anhui Univ Sci & Technol Sch Artificial Intelligence Huainan 232001 Peoples R China

The accuracy of state of charge estimation results will directly affect the performance of battery management system. Due to such, we focus in this article on the SOC estimation of Lithium-Ion batteries based on a fractional second-order RC model with free noninteger differentiation orders. For such an estimation, three Kalman filters are employed: the adaptive extended Kalman filter (AEKF), extended Kalman filter (EKF), and Unscented Kalman Filter (UKF). The Fractional-Order Model (FOM) parameters and differentiation orders are identified by the particle swarm Optimization (PSO) algorithm, and a pulsed-discharge test is implemented to verify the accuracy of parameter identification. The output voltage error of the FOM model is much less than that of the Integer-Order Model (IOM). The FOM model has lower root-mean square error (RMSE), the mean absolute error (MAE), and the maximum absolute error (MAXAE) of SOC estimation than the IOM model during the SOC estimation regardless of AEKF, EKF or UKF. Experimental results show that the FOM can simulate the polarisation on effect and charge-discharge characteristics of the battery more realistically, demonstrating that the SOC estimation based on FOM is more accurate and promising than the one based on the IOM when using the same Kalman filters.

关键词： Fractional calculus Lithium-Ion batteries state-of-charge particle swarm optimisation algorithm Kalman filter

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Increasing the ampacity of underground cable lines by optimising the thermal environment and design parameters for cable crossings

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第11期16卷 2309-2318页

作者： Perovic, Bojan Klimenta, Dardan Tasic, Dragan Raicevic, Nebojsa Milovanovic, Milos Tomovic, Milan Vukasinovic, Jovan Univ Pristina Kosovska Mitrovica Fac Tech Sci Kneza Milosa St 7 RS-38220 Kosovska Mitrovica Serbia Univ Nis Fac Elect Engn Nish Serbia Kosovo & Metohija Acad Appl Studies Dept Zvecan Zvecan Serbia

The problem of optimising the thermal environment and design parameters of underground cable lines for cable crossings with the aim of increasing the ampacities of cables is considered in this paper. particle swarm optimisation (PSO) algorithm, formulated as a continuous non-linear optimisation problem with constraints, for solving this hot spot problem is applied. It is found, using the PSO algorithm, that there are a suitable size of cable bedding and an arrangement of cables within that bedding, which can eliminate or significantly mitigate the hot spot effect without the use of any additional cooling equipment. In this manner, the ampacities of both crossing cable lines increase by about 15% on average with respect to the case of a similar crossing with installation parameters commonly used. In addition, it is shown how the cross-sectional areas of the conductors and metal screens and the metal screen bonding methods affect the optimal solution.

关键词： thermal environment conductors (electric) design parameters particle swarm optimisation algorithm hot spot problem power cables underground cables particle swarm optimisation cable bedding hot spot effect installation parameters crossing cable lines cross-sectional areas PSO algorithm metal screen bonding methods underground cable lines cooling continuous nonlinear optimisation problem

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