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Two redundant rule based algorithms for time-delay nonlinear models: least squares iterative and particle swarm optimisation

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2020年第3期35卷 258-264页

作者： Xu, Yuelin Rong, Yingjiao Sci & Technol Near Surface Detect Lab Wuxi 214028 Jiangsu Peoples R China

Two redundant rule based methods are developed for a time-delay nonlinear model in this paper. By using the redundant rule, the time-delay nonlinear model can be turned into a redundant model which contains some redundant terms. Then the least squares iterative and the particle swarm optimisation algorithms are applied to update the parameters and the corresponding time-delay. Compared with the redundant rule based least squares iterative algorithm, the redundant rule based particle swarm optimisation algorithm is more efficient for nonlinear models with complex structures. A simulation example shows that the proposed algorithms are effective.

关键词： nonlinear model particle swarm optimisation algorithm time-delay parameter estimation redundant rule least squares iterative

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A PSO-BP Target Localization Accuracy Enhancement Method for Airborne Optoelectronic Platforms 3

A PSO-BP Target Localization Accuracy Enhancement Method for...

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3rd International Conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Chen, Kuiyu Lei, Zhongkui Huang, Daqing Su, Nan Ling, Zhicheng College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Key Laboratory of Advanced Technology for Medium and Small UAV Nanjing210016 China College of Marxism Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9798350359961

This study aims to address the issue of target positioning accuracy in airborne optoelectronic platforms. Firstly, the paper refines and summarizes the typical workflow for target positioning based on UAV airborne optoelectronic platforms. To enhance the target positioning accuracy of airborne optoelectronic platforms, an improved method based on the particle swarm optimization algorithm (PSO) and the backpropagation neural network (BP) is proposed. The paper begins by explaining the research background and identifying the problems. It then proposes a target localization model based on the optoelectronic platform. Subsequently, the PSO-BP method is introduced, highlighting its advantages, principles, and implementation process. Experimental results demonstrate that the PSO-BP method can effectively enhance the target localization accuracy of airborne optoelectronic platforms. The research findings of this paper hold significant theoretical importance and practical value in improving the target localization accuracy of airborne optoelectronic platforms. Additionally, they offer a new research direction for further advancements in UAV target localization. In conclusion, this comprehensive literature review provides insights into the research gap and research motivation in the field of target positioning accuracy in airborne optoelectronic platforms. The proposed PSO-BP method demonstrates its effectiveness in enhancing target localization accuracy. The findings contribute to the theoretical understanding and practical application of improving target localization in airborne optoelectronic platforms, and they also suggest avenues for future research in UAV target localization. © 2023 IEEE.

关键词： Airborne optoelectronic platform BP neural network particle swarm optimisation algorithm Target positioning accuracy

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Simplified particle swarm algorithm based on nonlinear decrease extreme disturbance and Cauchy mutation

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2020年第3期35卷 236-245页

作者： Li, Jun Luo, Yangkun Wang, Chong Zeng, Zhigao Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan Peoples R China Wuhan Univ Sci & Technol Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan Peoples R China Hunan Univ Technol Coll Comp & Commun Xiangtan Hunan Peoples R China

Concerning the drawbacks that particle swarm optimisation algorithm is easy to fall into the local optima, and has low solution precision, the simplified particle algorithm which based on the nonlinear decrease extreme disturbance and Cauchy mutation is proposed. The algorithm simplifies particle updating formula, and uses logistic chaotic sequence to initialise the particle position, which can improve the global search ability of population;nonlinear decrease extreme disturbance strategy enhanced the diversity of the population and avoid the particles trapping in local optimum;a novel Cauchy mutation is used for the optimal particle variation to generate more optimal guiding particle movement. The experimental simulation on seven typical test functions shows that the proposed algorithm can effectively avoid falling into local optimal solution, the search speed and optimisation accuracy have improved significantly. The algorithm is suitable to solve the function optimisation problem.

关键词： particle swarm optimisation algorithm chaos nonlinear extreme disturbance simplified particle swarm Cauchy mutation

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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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An optimisation approach for uncertainty-based long-term production scheduling in open-pit mines using meta-heuristic algorithms

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INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT 2021年第2期35卷 115-140页

作者： Tolouei, Kamyar Moosavi, Ehsan Tabrizi, Amir Hossein Bangian Afzal, Peyman Bazzazi, Abbas Aghajani Islamic Azad Univ South Tehran Branch Dept Petr & Min Engn Tehran Iran Univ Kashan Dept Min Engn Kashan Iran

In mines planning, the long-term production scheduling problem (LTPSP) in open-pit mines is considered as a significant issue. It also specifies the distribution of cash flow during the course of the mine-life. Actually, LTPSP is a large-scale optimisation problem including large data-sets, multiple constraints, and uncertainty in the input factors that, has to be solved in a reasonable time. LTPSP, despite the valuable efforts of researchers, has not yet been well resolved. In this paper, hybrid models have been offered by the Lagrangian relaxation (LR) method with meta-heuristic methods, bat algorithm and particle swarm optimisation for solving the LTPSP due to the deterministic assumption and concerning the grade uncertainty. To bring update the Lagrange multipliers, the meta-heuristic algorithms have been applied. In terms of cumulative net present value, average ore grade, and computational time in a 12-year production period, the consequences achieved from the case studies point out that a solution close to optimisation can be presented by the LR-bat algorithm hybrid strategy in comparison with other methods. The results analysis has shown that the proposed method produces a near-optimal solution with a rational time that can be a good suggestion for utilising in the mining industry.

关键词： Open-pit mine long-term production scheduling grade uncertainty Lagrangian relaxation particle swarm optimisation algorithm bat algorithm

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Robust possibilistic programming for joint order batching and picker routing problem in warehouse management

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2021年第14期59卷 4434-4452页

作者： Attari, Mahdi Yousefi Nejad Torkayesh, Ali Ebadi Malmir, Behnam Jami, Ensiyeh Neyshabouri Islamic Azad Univ Dept Ind Engn Bonab Branch Bonab Iran Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Univ Virginia Dept Syst & Informat Engn Charlottesville VA USA Org Ind Mine & Trade Expert Ind East Azarbatjan Iran Virginia Tech Dept Business Informat Technol Pamplin Coll Business Blacksburg VA USA

Decisions made for designing and operating a warehouse system are of great significance. These operational decisions are strongly affected by total logistics costs, including investment and direct operating costs. The number of orders made by customers in the logistics section of warehouse management is very high because the number, type of products and items ordered by different customers vary broadly. However, machines layout for picking up products at logistics centres is minimal, inflexible, and, in some cases, inconclusive. In this study, we address joint order batching procedures of orders considering picker routing problem as a mixed-integer programming model. Extensive numerical experiments were generated in small, medium, and large sizes. In order to consider the uncertainty of parameters, we applied robust possibilistic programming for this problem. Three different meta-heuristic algorithms;genetic algorithm, particle swarm optimisation algorithm, and honey artificial bee colony algorithms are used as solution approaches to solve the formulated model. The performance of solution approaches over the problem was analysed using several test indexes. In all three group examples, there was no significant difference among mean values of the objective function, while there was a remarkable difference among computing times.

关键词： joint order batching picker routing genetic algorithm particle swarm optimisation algorithm artificial bee colony algorithms warehouse design warehousing systems

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Sports Image Feature Extraction Based on Machine Learning and Global Search algorithm

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2024年第0期

作者： Li, Haiting Yan, Jinghao Henan Univ Anim Husb & Econ Sports Dept Zhengzhou 450046 Peoples R China Shanghai Cyonet Infortek Ltd Shanghai 201619 Peoples R China

This study utilizes a combination of machine learning and a global search algorithm to enhance the quality of feature extraction in sports images. A deep residual generative adversarial network is employed to deblur images and sharpen their clarity, while the optimized particle swarm optimization algorithm is utilized to extract image features with precision and identify critical information. According to the experimental results, the research method improves the peak signal-to-noise ratios in ball sports image deblurring by 12.45%, 13.91%, and 17.18%, respectively, and in track and field sports image deblurring by 11.71%, 12.91%, and 21.61%, respectively, when compared with the generative adversarial network, generative adversarial network incorporating the attention mechanism, and multi-scale convolution-based algorithm. The accuracy-recall curve of the particle swarm algorithm that has been optimised for research completely encircles the accuracy-recall curves of the other four algorithms, which verifies the efficacy of the research methodology. The research will offer a more comprehensive perspective on sports image processing.

关键词： Sports images feature extraction deblurring deep residual generative adversarial networks particle swarm optimisation algorithm

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Design of the fractional order internal model controller using the swarm intelligence techniques for the coupled tank system

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2021年第2期29卷 1207-1225页

作者： Vavilala, Sateesh Kumar Thirumavalavan, Vinopraba Thota, Radhakrishnan Natarajan, Sivakumaran NIT Puducherry Dept Elect & Elect Engn Karaikal India NIT Tiruchirapalli Dept Chem Engn Trichy India NIT Tiruchirapalli Dept Instrumentat & Control Engn Trichy India

The coupled tank system (comprising two tanks) is used in the chemical industries, water treatment plants etc. Level control of the coupled tank system is a common problem in the process control industry. This work proposes a fractional order internal model controller (FOIMC) with a higher order fractional filter for the level control of the coupled tank system. A first order plus delay time (FOPDT) model of the system is used in the controller design. FOIMC has advantages like robustness to changes in the system gain and extended stability margins. The proposed higher order fractional filter makes the controller physically realizable and quickly roll off the magnitude Bode plot, neglecting the high frequency noise. The particle swarm optimisation (PSO) algorithm is a swarm intelligence based algorithm used for the optimisation problems. The parameters of the FOIMC are optimized with the PSO algorithm by minimizing an objective function constructed using time domain specifications. The novel objective function includes weighted peak overshoot, settling time, and integral square error. A MATLAB (MathWorks, Inc., Natick, MA, USA) based tool, fractional order modelling and control (FOMCON) is used to simulate the fractional order controller. Performance of the proposed FOIMC is compared with two state of the art. Robustness to change in the operating point (tank height) is verified. The proposed FOIMC and the state of the art controllers are implemented on the laboratory setup, and the experimental results are compared.

关键词： Fractional order internal model controller particle swarm optimisation algorithm higher order fractional filter coupled tank system

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Research on Supply Chain optimisation Management Method Integrating Employee Behaviour Factors by Improving PSO algorithm

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2024年第1期23卷

作者： Li, Wenhui Wang, Can Zhengzhou Railway Vocat & Tech Coll Sch Transportat Management Zhengzhou 451460 Peoples R China Zhengzhou Railway Vocat & Tech Coll Sch Artificial Intelligence Zhengzhou 451460 Peoples R China

With the continuous advancement of the trend of economic globalisation and the in-depth development of personalised services, the manufacturing mode has begun to change to service-oriented manufacturing, and the focus of enterprises has gradually shifted from the industrial chain to the supply chain. However, at present, accidents often occur in the supply chain around products, such as changes in orders, lack of resources in a short period of time, etc. These interference events are difficult to control and cause great damage to the normal operation and economic interests of enterprises for a long time. Therefore, it is necessary to study the optimisation methods of enterprise supply chain. Therefore, it is necessary to study the optimisation methods of enterprise supply chain. The study uses system dynamics to analyses employee counterproductive behaviour, develops a disturbance management model incorporating employee behavioural factors, and solves it with an improved particle swarm optimisation (PSO) algorithm. The experimental results show that the maximum number of noninferior solutions obtained by the improved PSO algorithm is 14 and 12, respectively. Compared with the GA_TOM (Genetic algorithm_TOM), the improved algorithm is closer to the ideal pareto front. In the MS index, the average and minimum values obtained by the improved PSO algorithm are 0.57 and 0.609, respectively, which can cover more ideal pareto fronts. It shows that the algorithm effectively improves the stability and security of the supply chain, and provides a practical reference for the supply chain optimisation of manufacturing enterprises.

关键词： Anti-production behaviour particle swarm optimisation algorithm supply chain interference management

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particle swarm approach to the optimisation of trenched cantilever-based MEMS piezoelectric energy harvesters

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2019年第4期13卷 582-588页

作者： Kordrostami, Zoheir Roohizadegan, Sajjad Shiraz Univ Technol Dept Elect Engn Shiraz Iran

A micro-electro-mechanical system (MEMS) trenched piezoelectric energy harvester based on a cantilever structure has been proposed. The trenched piezoelectric layer has increased the output voltage and the generated power. It also provides three additional design parameters such as the trench position, depth and length. A particle swarm approach has been used for optimisation of the piezoelectric energy harvester geometry with the aim of finding the optimum design which transfers the maximum harvested power to a definite load. The optimisations and comparisons have been made for unimorph, bimorph, trenched and non-trenched cantilever beams. The results are quite revealing that the generated power for a trenched bimorph energy harvester is much larger than other structures. The optimum design found by particle swarm optimisation algorithm has asymmetric trenches in the top and bottom piezoelectric layers and can generate much more power than the unoptimised structure.

关键词： micromechanical devices piezoelectric transducers cantilevers particle swarm optimisation energy harvesting optimisation particle swarm approach piezoelectric energy harvester geometry trenched bimorph energy harvester particle swarm optimisation algorithm piezoelectric layers trenched cantilever-based MEMS piezoelectric energy harvesters microelectro-mechanical system cantilever structure piezoelectric layer

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