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Optimal capacity allocation of standalone wind/solar/battery hybrid power system based on improved particle swarm optimisation algorithm

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IET RENEWABLE POWER GENERATION 2013年第5期7卷 443-448页

作者： Wang, Jidong Yang, Fan Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China Tianjin Univ Sch Elect Engn & Automat Tianjin 300072 Peoples R China

A standalone wind/solar/battery hybrid power system, making full use of the nature complementarity between wind and solar energy, has an extensive application prospect among various newly developed energy technologies. The capacity of the hybrid power system needs to be optimised in order to make a tradeoff between power reliability and cost. In this study, each part of the wind/solar/battery hybrid power system is analysed in detail and an objective function combining total owning cost and loss of power supply probability is built. To solve the problems with non-linearity, complexity and huge computation, an improved particle swarm optimisation (PSO) algorithm is developed, which integrates the taboo list to broaden the search range and introduces 'restart' and 'disturbance' operation to enhance the global searching capability. The simulation results indicate that the proposed algorithm is more stable and provides better results in solving the optimal allocation of the capacity of the standalone wind/solar/battery hybrid power system compared with the standard PSO algorithm.

关键词： battery storage plants hybrid power systems particle swarm optimisation power generation economics power generation reliability solar power stations wind power plants optimal capacity allocation standalone wind-solar-battery hybrid power system particle swarm optimisation algorithm wind energy solar energy power reliability power cost power supply probability PSO algorithm

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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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A self-organisation particle swarm optimisation algorithm based on L norm multi-measurements diversity feedback

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2013年第2-3期47卷 262-272页

作者： Jiang, Shanhe Ji, Zhicheng Jiangnan Univ Inst Elect Automat Key Lab Adv Proc Control Light Ind Minist Educ Wuxi Peoples R China Anqing Normal Coll Dept Phys & Power Engn Anqing Peoples R China Jiangnan Univ Inst Elect Automat Wuxi Peoples R China

To improve the sole perception method of population diversity and premature stagnation, a self-organisation particle swarm optimisation algorithm based on L norm multi-measurements diversity feedback (SOPSO-L) is proposed, which introduces negative feedback mechanism to imitate the information interaction between the individuals. Position diversity, velocity diversity and self-cognitive diversity based on L norm are defined as perception information of the swarm. The proposed algorithm adopts multi-measurements swarm diversity as dynamic perception information to tune key parameters such as inertia weight and acceleration coefficients to make the algorithm in convergence or divergence stage. The corresponding characteristics of population diversities were studied. SOPSO-L is tested on six typical test functions and is compared to other variants of PSO presented in the literature. The results show that the proposed method not only greatly improves the global searching capability and computational efficiency, but also effectively avoids the local stagnation problem.

关键词： particle swarm optimisation algorithm premature stagnation self-organisation multi-measurements diversity L norm feedback

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A new localization method based on improved particle swarm optimization for wireless sensor networks

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IET SOFTWARE 2022年第3期16卷 251-258页

作者： Yang, Qiaohe Shanghai Univ Sch Commun & Informat Engn Key Lab Special Fiber Opt & Opt Access Networks Shanghai Peoples R China

Wireless sensor network (WSN) node localisation technology based on received signal strength indication (RSSI) is widely used as it does not need additional hardware devices. The ranging accuracy of RSSI is poor, and the particle swarm optimisation (PSO) algorithm can effectively improve the positioning accuracy of RSSI. However, the particle swarm diversity of the PSO algorithm is easy to lose quickly and fall into local optimal solution in the iterative process. Based on the convergence conditions and initial search space characteristics of the PSO algorithm in WSN localisation, an improved PSO algorithm (improved self-adaptive inertia weight particle swarm optimisation [ISAPSO]) is proposed. Compared with the other two PSO location estimation algorithms, the ISAPSO location estimation algorithm has good performance in positioning accuracy, power consumption and real-time performance under different beacon node proportions, node densities and ranging errors.

关键词： improved PSO algorithm WSN localisation node densities improved particle swarm optimization particle swarm diversity ranging accuracy wireless sensor networks sensor placement ranging errors mobile radio particle swarm optimisation additional hardware devices initial search space characteristics ISAPSO location estimation algorithm different beacon node proportions localization method received signal strength indication self-adaptive inertia weight particle swarm optimisation PSO location estimation algorithms positioning accuracy particle swarm optimisation algorithm search problems RSSI wireless sensor network node localisation technology local optimal solution

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Research on an optimisation control method of large-scale buildings energy saving based on particle swarm optimisation

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2022年第2-3期44卷 166-181页

作者： Wen, Xiaolong Hunan City Univ Coll Civil Engn Yiyang 413000 Hunan Peoples R China

Aiming at the problems of high energy consumption and low day-lighting coefficient in traditional building energy-saving control methods, an energy-saving optimisation control method for large-scale buildings based on particle swarm optimisation is proposed. Using Autodesk Revit in BIM modelling software the software constructs the large-scale building model, extracts the characteristics of large-scale building organisation information by SIFT method;uses multiple linear regression analysis method to obtain the large-scale building model wall, external window heat transfer coefficient and other parameters, completes the large-scale building operation state analysis;uses particle swarm optimisation algorithm to optimise the large-scale building energy-saving parameters, and obtains its objective function to obtain the large-scale construction Building the optimal energy consumption parameters to achieve large-scale building automation energy-saving control. The experimental results show that: after the energy-saving control of large-scale buildings, the day-lighting coefficient is higher.

关键词： BIM technology large-scale building automation energy saving control multiple linear regression particle swarm optimisation algorithm

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Ore blending scheme optimization of copper smelting based on improved PSO algorithm

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CANADIAN METALLURGICAL QUARTERLY 2025年

作者： Li, Shilei Zhu, Daofei Yang, Yingbao Fu, Yanxiong Yan, Tao Kunming Univ Sci & Technol Fac Met & Energy Engn Kunming 650093 Peoples R China Yimen Copper Co Ltd Yuxi Peoples R China

In copper smelting, the ore blending scheme is crucial for product quality and cost. Traditional methods, relying on manual experience, have limitations and can't reach the optimal. This study thus presents an intelligent ore blending method. It starts by constructing a mathematical model. The objective function covers three aspects: minimising the cost of raw materials entering the furnace, minimising deviation of elemental content of raw materials entering the furnace from set values, and minimising deviation of the total weight of the ore blend from the set value. Constraint conditions consider production processes to ensure model feasibility. An improved PSO algorithm, with a linearly decreasing inertia weight and constriction factor method, is designed to solve the model. Tests using two sets of data from a large copper smelting enterprise show that for the same Cu and S contents, the cost of raw materials entering the furnace decreased by 40.044 yuan and 35.186 yuan per ton, respectively. Also, the intelligent method converges quickly, getting an optimised scheme in about 20s. This reduces ore blending workload, improves efficiency, cuts costs, and brings economic benefits to the enterprise. Dans le proc & eacute;d & eacute;de fusion du cuivre, le sch & eacute;ma de m & eacute;lange du minerai joue un r & ocirc;le d & eacute;cisif dans la qualit & eacute;et le co & ucirc;t du produit. Cependant, les m & eacute;thodes traditionnelles de m & eacute;lange du minerai se fondent principalement sur l'exp & eacute;rience manuelle et pr & eacute;sentent des limites importantes, ce qui rend difficile l'obtention d'un sch & eacute;ma de m & eacute;lange du minerai proche de l'optimal. Par cons & eacute;quent, cette & eacute;tude propose une m & eacute;thode intelligente de m & eacute;lange du minerai. En premier, cette m & eacute;thode construit un mod & egrave;le math & eacute;matique dont la fonction objective comprend trois aspects: premi & egrave;rement, le co & uci

关键词： Multi-objective nonlinear programming model particle swarm optimisation algorithm ore blending plan linearly decreasing inertia weight constriction factor method

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Prediction of permeability characteristics of mine slurries and sediments using finite-strain framework by optimisation algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 261卷

作者： Vasudev, Akhila Bharat, Tadikonda Venkata Indian Inst Technol Guwahati Dept Civil Engn Gauhati 781039 Assam India

Determining the permeability characteristics of mine tailing slurries through laboratory finite-strain consolidation tests is costly due to the extensive testing required. An alternative approach involves back-predicting hydraulic conductivities from settling column test data. This study employed settlement and excess pore pressure data as inputs in optimising the permeability parameters using the finite-strain consolidation (FSC) framework. The proposed approach used the finite difference method to solve the governing equation for FSC in the forward analysis. Two novel variants of the Artificial Bee Colony (ABC) algorithm, namely the Modified Artificial Bee Colony (MABC) and the Hybrid Artificial Bee Colony (HABC) algorithms, were used to back-predict the hydraulic conductivity function parameters from the FSC test data. These models predicted the permeability characteristics using the temporal variation of the settlements for six slurries and excess pore pressure dissipation data for four slurries. The performance of the proposed algorithms along with Hybrid particle swarm optimisation (HPSO) was compared with the conventional optimisation algorithms, viz., PSO, ABC, and Quantum PSO (QPSO). The HABC and HPSO exhibited remarkable stability, consistently converging to identical solution sets across multiple iterations, thereby outperforming other algorithms in this study. Despite its higher time complexity by HABC relative to the other evaluated methods, this complexity is warranted for enhanced robustness. The current study is helpful in brining practical and reliable methodologies for estimating the hydraulic conductivity function from field-scale conductivity (FSC) data, providing critical insights for the safe and efficient management of slurry wastes.

关键词： Finite strain consolidation Hydraulic conductivity function particle swarm optimisation algorithm Artificial bee colony algorithm Inverse analysis

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Design of intermediate roll contour and analysis of regulation ability on strip shape of 18-High mill

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第12期131卷 6139-6151页

作者： Zhao, Jun Sun, Jing-Na Wu, Tong Guo, Chao-Jian Huang, Hua-Gui Yanshan Univ Natl Engn Res Ctr Equipment & Technol Cold Strip R Qinhuangdao 066004 Hebei Peoples R China

The corresponding objectives and principles of the intermediate roll contour design are proposed to improve the strip edge drop problem in the process of 18-High mill production and enhance the regulation ability on strip shape. The intermediate roll contour is designed using the particle swarm optimisation algorithm to satisfy the production requirements. The elastic-plastic coupling finite element model of roll system-rolling piece integration is established, the application effect of the new roll contour is verified, and the regulation ability on strip shape under the new roll contour is analysed. Results show that, compared with the original roll contour, the strip edge drop phenomenon is remarkably improved. The regulation ability on strip shape is evidently improved compared with that of the original roll contour. The regulation ability on strip shape of the intermediate roll shifting is stronger than that of the intermediate roll bending force under the new roll contour. Finally, combined with field data of rolling, the finite element model is verified on site. The simulation and measured values are high, and the error is within 10%. The research results can provide a good theoretical basis for the improvement of the intermediate roll contour and improvement of the regulation ability on strip shape of 18-High mill.

关键词： 18-High mill Intermediate roll contour particle swarm optimisation algorithm Strip shape Regulation ability

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Hybrid small-signal model parameter extraction for GaN HEMT based on QGA

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INTERNATIONAL JOURNAL OF ELECTRONICS 2024年第4期111卷 729-747页

作者： Wang, Shaowei Zhang, Jincan Yang, Shi Liu, Min Wang, Jinchan Zhang, Juwei Henan Univ Sci & Technol Elect Engn Coll Luoyang 471003 Peoples R China Novaco Microelect Technol Ltd Dept Res & Dev Nantong Peoples R China

A novel algorithm is proposed for optimal extraction of GaN HEMT small-signal model parameters. The proposed Quantum Genetic algorithm (QGA) exploits the superposition, entanglement and interference of quantum states, which solves the problems of high number of iterations and slow convergence when obtaining optimal solutions using Genetic algorithms (GA). Meanwhile, it is solved that the particle swarm optimisation (PSO) algorithm produces premature convergence and easily falls into the local optimum solution. In order to avoid the influence of distributed parasitic effects in large size devices under high-frequency conditions, a suitable frequency range is determined and combined with direct extraction techniques to determine the range of parameter values. The model parameter values are optimised step by step using QGA. In order to verify the superiority of QGA, QGA and PSO algorithms are both used to optimise GaN HEMT small-signal model parameters. By comparing the modelled S-parameter effects of the QGA and the PSO algorithm, it can be found that the QGA has better consistency with the measured data.

关键词： Quantum genetic algorithm genetic algorithm particle swarm optimisation algorithm GaN HEMT small-signal model

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Optimal pricing and inventory strategies for leased equipment considering lessees' options

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第19期62卷 7261-7278页

作者： Liu, Yanping Liu, Biyu Yang, Haidong Luo, Kai Fuzhou Univ Sch Econ & Management Fuzhou Peoples R China Fuzhou Univ Logist Res Ctr Fujian Social Sci Res Base Fuzhou Peoples R China Paris Sch Business Paris France Fuzhou Univ Fuzhou Univ Town Sch Econ & Management 2 Wulong Jiangbei Ave Fuzhou Fujian Peoples R China

After the expiration of each lease, lessees may return leased equipment on schedule, renew the lease or purchase it according to the equipment status and their demands. By considering lessees' uncertain options and equipment status difference, the pricing and inventory decision-makings of leased equipment are explored. A mixed-integer nonlinear programming model by maximising the net present value of lessor's profit is presented with respect to constraints like rental revenue, manufacturing, lessees' credit check, transportation, maintenance, upgrade and inventory costs. The rental price and inventory decisions are obtained by solving the problem with a particle swarm optimisation algorithm. We also analyse the impacts of purchasing cost of old equipment from a third-party supplier on lessor's inventory, renewal price or purchasing price of leased equipment on lessees' options and lessor's profit. The results show: (1) with the extension of lease period, the rental price increases while the growth rate decreases;(2) the maintenance cost accounts for about 20% of total cost, and the preventive maintenance strategy can reduce excess maintenance cost as lease period increases;(3) the lessor shall set moderate renewal price discount coefficient and purchasing price coefficient, and analyse purchasing cost of old equipment to manage inventory timely.

关键词： Pricing and inventory leasing lessees' options preventive maintenance particle swarm optimisation algorithm

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