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A new improved particle swarm algorithm for optimization of anchor lattice beam support structures

NATURAL HAZARDS 2025年第1期121卷 225-243页

作者： Liu, Yang He, Jintao Liang, Jiong Wang, Wentao Wang, Zimu Xian Polytech Univ Sch Urban Planning & Municipal Engn Xian 710048 Peoples R China Chinese Acad Geol Sci Inst Explorat Technol Chengdu 611734 Peoples R China Univ Michigan Dept Civil & Environm Engn Ann Arbor MI 48109 USA

Landslides are one of the most common natural disasters. Supporting structure plays an important role in landslide control. Slope stabilization with the application of anchor lattice beams has drawn considerable attention. However, existing structural design approaches tend to be conservative, and solutions for optimal anchor grid design are demanding. In this study, an object-oriented computer program was developed by using Python to optimize anchor lattice beam parameters in slope construction. The program utilized an improved particle swarm optimization (PSO) algorithm. It served as an efficient way to figure out the optimal parameter combination to enhance embankment construction design quality and safety. The PSO-based optimization demonstrated significant improvements in slope stability and safety, resulting in up to 30.5% average enhancement compared to non-optimized designs. Sensitivity analyses on the distance of the anchor, prestress, and angle of the anchor revealed the influence of each parameter on leading the way to appropriate anchorage conditions for anchor lattice beam support structures.

关键词： improved particle swarm algorithm Anchor lattice beam structure Support structures Optimization

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Study on the Optimal Allocation of Energy Storage Capacity for Stand-Alone Photovoltaic Power Generation System Based on improved particle swarm algorithm

Study on the Optimal Allocation of Energy Storage Capacity f...

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2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Huo, Shutong Jiang, Jiaju Fan, Yiran Wang, Shilin Li, Weilun Li, Jie Xi'an Polytechnic University Shaanxi Xi'an China State Grid Anhui Electric Power Co. Ltd. Hefei Power Supply Company Anhui Hefei China

ISBN: (纸本)9798331504205

With the rapid advancement of renewable energy, photovoltaic power generation has become a crucial global source of electricity. However, the temporal and fluctuating nature of light conditions presents a challenge in terms of energy storage needs for photovoltaic systems. Addressing this issue, standalone photovoltaic systems require efficient energy storage configurations to ensure the stability and reliability of power supply. Enhanced particle swarm Optimization, owing to its formidable optimization capabilities, emerges as an ideal choice for optimizing energy storage capacity. Against this backdrop, the optimization of photovoltaic-storage system models not only facilitates the implementation of economically viable energy storage solutions but also propels the sustainable development of renewable energy, thereby holding significant implications for achieving carbon neutrality. © 2025 IEEE.

关键词： energy storage capacity improved particle swarm algorithm optimal allocation power generation system stand-alone photovoltaic

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Application of improved particle swarm optimization algorithm combined with genetic algorithm in shear wall design

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SYSTEMS AND SOFT COMPUTING 2025年 7卷

作者： Gao, Wei Hebei Software Inst Dept Digital Media Baoding 071000 Peoples R China

In order to achieve the rationality and economy of shear wall layout, an improved algorithm was designed in the architectural design program. The improved algorithm is based on the basic framework of genetic algorithm and particle swarm optimization algorithm, first adjusting the inertia weight, and then introducing elimination mechanism and mutation rate control. A shear wall design model was constructed using an improved algorithm, which was applied to determine the layout of shear walls in a 28 story high-rise building in a certain city. The example results show that when using the designed shear wall design program for scheme design, the success rate reaches 100 %, which is 38.47 % higher than the original particle swarm optimization algorithm. The obtained optimization scheme has interlayer displacement angles of 1/2096 and 1/1800 in the vertical and horizontal directions, respectively, while the torsional displacement ratio in both directions is 1.0908 and the torsional period ratio is 0.7125. After optimizing the algorithm, the length of the shear wall material was saved by 10.97 %, effectively reducing the use of materials. This not only reduces construction costs, but also brings higher space utilization efficiency. The building design scheme obtained from this study not only meets national standards, but also has lower computational time costs. This study demonstrates the potential application of this design algorithm in solving traditional architectural design problems. This not only provides new tools for the field of architectural design, but also stimulates more interdisciplinary cooperation, integrating computer science, artificial intelligence technology more closely with building engineering.

关键词： Genetic algorithm improved particle swarm algorithm Architectural design Shear wall

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High-speed elevator car system semi-active horizontal vibration reduction method based on the improved particle swarm algorithm

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JOURNAL OF VIBRATION AND CONTROL 2023年第13-14期29卷 2921-2934页

作者： Wang, Zili Qiu, Lemiao Zhang, Shuyou Su, Guannan Zhu, Linhao Zhang, Xuewei Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Yuquan Campus 1 Bldg Room 406 Hangzhou 310000 Zhejiang Peoples R China Zhejiang Univ Engn Res Ctr Design Engn & Digital Twin Zhejiang Hangzhou Zhejiang Peoples R China Canny Elevator Co Ltd Suzhou Peoples R China

As the field of modern high-rise buildings advances, the demand for the high-speed elevator increases. The high-speed elevator car vibration directly affects the riding comforts, among which the high-speed elevator car horizontal vibration has the most pronounced effect. Previous researches indicate that the semi-active vibration reduction method is an effective horizontal vibration reduction method for elevator car, which has the characteristics of low energy consumption, suitable for high-speed elevator, while its adaptability is relatively weak. To address the adaptability issue, we proposed a semi-active high-speed elevator car horizontal vibration reduction method based on the improved particle swarm algorithm. To verify the feasibility of the proposed method, it is applied with a magneto-rheological damper in the KLK2-type elevator from Canny Elevator Co, Ltd The horizontal vibration results are compared with ones of the passive vibration reduction method and other algorithm-based vibration reduction methods. The vibration reduction method proposed in this paper is superior to all other mentioned methods in terms of the peak-to-peak value and the A95 value of the vibration acceleration. The horizontal vibration acceleration performance of the HSEC is improved by about 70%.

关键词： high-speed elevator car semi-active control horizontal vibration reduction improved particle swarm algorithm LQG controller

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Optimization of cable-stayed force for asymmetric single tower cable-stayed bridge formation based on improved particle swarm algorithm

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INTERNATIONAL JOURNAL OF STRUCTURAL INTEGRITY 2024年第5期15卷 873-901页

作者： Xiong, Hui-Zhong Yang, Xin He, Yong-Nan Huang, Yong Northeast Forestry Univ Sch Civil Engn & Transportat Harbin Peoples R China Heilongjiang Prov Transportat Infrastructure Const Harbin Peoples R China China Earthquake Adm Inst Engn Mech Harbin Peoples R China

PurposeThis paper aims to optimize cable-stayed force in asymmetric one-tower cable-stayed bridge formation using an improved particle swarm algorithm. It compares results with the traditional unconstrained minimum bending energy ***/methodology/approachThis paper proposes an improved particle swarm algorithm to optimize cable-stayed force in bridge formation. It formulates a quadratic programming mathematical model considering the sum of bending energies of the main girder and bridge tower as the objective function. Constraints include displacements, stresses, cable-stayed force, and uniformity. The algorithm is applied to optimize the formation of an asymmetrical single-tower cable-stayed bridge, combining it with the finite element *** study's findings reveal significant improvements over the minimum bending energy method. Results show that the structural displacement and internal force are within constraints, the maximum bending moment of the main girder decreases, resulting in smoother linear shape and more even internal force distribution. Additionally, the tower top offset decreases, and the bending moment change at the tower-beam junction is reduced. Moreover, diagonal cable force and cable force increase uniformly with cable length ***/valueThe improved particle swarm algorithm offers simplicity, effectiveness, and practicality in optimizing bridge-forming cable-staying force. It eliminates the need for arbitrary manual cable adjustments seen in traditional methods and effectively addresses the optimization challenge in asymmetric cable-stayed bridges.

关键词： Bridge engineering Single-tower cable-stayed asymmetric bridge Reasonable bridge formation status FEM Secondary planning improved particle swarm algorithm

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Path planning of underwater vehicle based on improved particle swarm algorithm 36

Path planning of underwater vehicle based on improved partic...

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36th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Lufei Ge, Yuan Guan, Zhihong Ye, Gang Feng, Haoyu Anhui Ploytech Univ Collage Elect Engn Wuhu Peoples R China Huazhong Univ Sci & Technol Collage Automat Wuhan Peoples R China Suzhou USTAR Intelligent Technol Co Ltd Suzhou Peoples R China

ISBN: (纸本)9798350387780;9798350387797

particle swarm algorithm can overcome the shortcomings of insufficient optimization ability, large calculation amount and difficulty in planning the optimal path of traditional algorithms in the path planning of underwater vehicles with complex processing environment and many constraints. However,traditional particle swarm algorithms have drawbacks such as low accuracy and easy falling into local optima. In order to solve this problem, This paper sets an inertia weight that decreases with the number of iterations and an asynchronous shrinkage learning factor for the particle swarm algorithm under the condition of converting from a spatial coordinate system to a spherical coordinate system and then applies it to the path planning of underwater *** experiments show that the improved particle swarm algorithm (LINW-SPSO) proposed in this paper is superior to the traditional particle swarm algorithm in terms of algorithm stability and path planning effect.

关键词： underwater vehicles path planning inertia weights learning factors improved particle swarm algorithm

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Multi-Objective Optimal Scheduling of Microgrids Based on improved particle swarm algorithm

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ENERGIES 2024年第7期17卷 1760页

作者： Guan, Zhong Wang, Hui Li, Zhi Luo, Xiaohu Yang, Xi Fang, Jugang Zhao, Qiang Wudian New Energy Co Ltd Wuhu City Wuhu 241012 Peoples R China Tsinghua Univ Sichuan Energy Internet Res Inst Chengdu 610000 Peoples R China China Power Gharmony Energy Technol Co Ltd Beijing 102488 Peoples R China

Microgrid optimization scheduling, as a crucial part of smart grid optimization, plays a significant role in reducing energy consumption and environmental pollution. The development goals of microgrids not only aim to meet the basic demands of electricity supply but also to enhance economic benefits and environmental protection. In this regard, a multi-objective optimization scheduling model for microgrids in grid-connected mode is proposed, which comprehensively considers the operational costs and environmental protection costs of microgrid systems. This model also incorporates improvements to the traditional particle swarm optimization (PSO) algorithm by considering inertia factors and particle adaptive mutation, and it utilizes the improved algorithm to solve the optimization model. Simulation results demonstrate that this model can effectively reduce electricity costs for users and environmental pollution, promoting the optimized operation of microgrids and verifying the superior performance of the improved PSO algorithm. After algorithmic improvements, the optimal total cost achieved was CNY 836.23, representing a decrease from the pre-improvement optimal value of CNY 850.

关键词： microgrid multi-objective improved particle swarm algorithm optimal scheduling

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Modal Optimization Design of Supporting Structure Based on the improved particle swarm algorithm

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INTERNATIONAL JOURNAL OF ENGINEERING 2022年第4期35卷 740页

作者： Shijing, D. Hongru, C. Xudong, W. Deshi, W. Yongyong, Z. Naval Univ Engn Wuhan Peoples R China Chinese Peoples Liberat Army Navy Unit 92199 Qingdao Peoples R China Chinese Peoples Liberat Army Navy Unit 92064 Dongguan Peoples R China

To cope with the strong vibration of a supporting structure excited by external loads under operating conditions, and in order to achieve the purpose of vibration reduction by structural optimization through modal modification, a modal modification method was proposed, through structural vibration theory. Subsequently, the search performance of an improved particle swarm optimization method was analyzed before conducting a case study on the structural optimization. Finally, aiming at the problem of strong vibration of gun mount at the time of firing, a finite element model of the gun mount was constructed and the type and natural frequency of the gun vibration in a free state was analyzed. Meanwhile, taking the thickness, height and width of the stiffening structure of the bracket as the design variables, combined with the improved particle swarm algorithm, an optimized mathematical model was developed with the first-order natural frequency of the gun mount as the objective function. The secondary development of Abaqus finite element software by using Python is used as a tool to calculate the optimization model. By virtue of optimation, thickness, width and height of the stiffening structure are 156.4mm, 453.7mm and 238.9mm at the range of [100,600]mm, [100,700]mm, [100,700]mm, respectively, and the base frequency of the gun mount has been increased by 11.3%. The effect is remarkable.

关键词： Supporting Structure Modal Analysis Finite Element improved particle swarm algorithm Gun Mount Modal Modification

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Optimal Control of Hybrid Energy Storage System of New Energy Power Generation System Based on improved particle swarm algorithm

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JOURNAL OF INTERCONNECTION NETWORKS 2022年第3期22卷 2145004-2145004页

作者： Zhang, Shangzhou Shangluo Univ Coll Elect Informat & Elect Engn Shangluo 726000 Shaanxi Peoples R China

In order to ensure the stability and reliability of power supply and realize day and night power generation, wind and solar complementary power generation systems are built in areas with abundant solar and wind energy resources. However, the system investment cost is too high. Because of this, there are wind, light intermittent, and non-intermittent power generation systems. For issues such as stability, an energy storage system needs to be configured to stabilize power fluctuations. This paper aims to study the optimization control of hybrid energy storage system of new energy power generation system based on improved particle swarm algorithm. In this paper, the application of particle swarm algorithm to power system reactive power optimization has been researched in two aspects. Through optimization methods, reasonable adjustment of control variables, full use of equipment resources of the power grid, to improve voltage quality and reduce system operation network to ensure the stability of the voltage system. In addition, this paper selects the IEEE30 node test system and simulation data analysis, takes the hybrid energy storage system as the optimization object, and optimizes the reactive power of the newly improved particle swarm algorithm. The experiments in this paper show that the improved algorithm has a good effect in reactive power optimization, increasing the performance of the hybrid energy storage system by 27.02%. MPSO algorithm is also better than basic PSO algorithm. It can be seen from the figure that in the PSO algorithm, the algorithm basically tends to be stable after more than 40 iterations, and finally the algorithm converges to 0.089.

关键词： improved particle swarm algorithm new energy power generation system hybrid energy storage system optimized control

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Reactive Power Optimization for Power System With Wind Farm Based on improved particle swarm algorithm

Reactive Power Optimization for Power System With Wind Farm ...

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第30届全国化学与物理电源学术年会

作者： ZHANG Han-ping Computer Department Wuhan Polytechnic

The problem of power system reactive power optimization is a mathematical problem with multiple variables and constraints,which is complex and *** control variables include continuous variables and discrete variables,it's difficult to get the optimal solution.A solution in power flow calculation is put forward after the mathematical model of wind farm is built in this *** on this,taking the WSCC 9 nodes system as an example,use the particle swarm algorithm to solve the reactive power optimization *** result shows that this algorithm has an apparent positive effect on reducing system power loss and improving system voltages.

关键词： improved particle swarm algorithm wind farm reactive power optimization

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