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A Modified particle swarm optimization algorithm for the Design of a Double-Pad Aerostatic Bearing With a Pocketed Orifice-Type Restrictor

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JOURNAL OF TRIBOLOGY-TRANSACTIONS OF THE ASME 2014年第2期136卷 021701-021701页

作者： Chang, S. H. Jeng, Y. R. Natl Chung Cheng Univ Dept Mech Engn Adv Inst Mfg High Tech Innovat Chiayi 62102 Taiwan

The performance of an aerostatic bearing with a pocketed orifice-type restrictor is affected by the bearing size, pocket size, orifice design, supply pressure, and bearing load. This study proposes a modified particle swarm optimization (MPSO) algorithm to optimize a double-pad aerostatic bearing. In bearing optimization, the upper and lower bearing designs are independent and several design variables that affect bearing performance must be considered. This study also applies the concept of mutation from a genetic algorithm. The results show that the MPSO algorithm has a global search capability and high efficiency to optimize a problem with several design variables and that the mutation can provide an avenue for particles to escape from a local optimal value.

关键词： aerostatic bearing particle swarm optimization algorithm orifice-type restrictor

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optimization of 3D printing process parameters to minimize surface roughness with hybrid artificial neural network model and particle swarm algorithm

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PROGRESS IN ADDITIVE MANUFACTURING 2021年第2期6卷 199-215页

作者： Shirmohammadi, Mohammad Goushchi, Saeid Jafarzadeh Keshtiban, Peyman Mashhadi Urmia Univ Technol Fac Ind Engn Orumiyeh Iran Urmia Univ Technol Fac Mech Engn POB 57155-419 Orumiyeh Iran

Due to the significant impact on the product quality and performance, the surface roughness of produced parts by 3D printers is one of the important factors in the 3D printing process. Then, the main objective of this research is to determine the optimal composition of input parameters to minimize surface roughness, using hybrid artificial neural network and particle swarm algorithm. For this purpose, after using Central Composite Design (CCD) of experiments with five independent parameters (nozzle temperature, layers thickness, printing speed, nozzle diameter and material density) with three levels, 43 flat parts were produced with a three-dimensional printer, and roughness tests were performed on produced parts. After training experimental matrix by multilayer perceptron neural network (7-4-1) with a coefficient of 0.95, the subjected matrix was combined with the particle swarm algorithm to determine the optimal composition of input parameters. To verify results accuracy, the optimized process parameters obtained from the combined algorithm, have been tested with experimental results. In addition, to specify the effect of input parameters on the surface roughness, a quadratic model has been developed using Response Surface Method (RSM). Based on results of the hybrid algorithm, the optimal combination of input parameters was extracted. It was inferred that, the nozzle temperature of 192.20 degrees C, the layers' thickness of 100 mu m, the printing speed of 97.06 mm/s, the nozzle diameter of 0.3 mm and the internal density of 24.88% lead to the surface roughness of 11.319. Therefore, the use of this hybrid algorithm improves the surface quality of the printed parts during the 3D printing process.

关键词： 3D printing Surface roughness Artificial neural network particle swarm optimization algorithm Response surface method

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Chaotic Butterfly optimization algorithm based on particle swarm optimization

International Core Journal of Engineering

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International Core Journal of Engineering 2022年第3期8卷 420-432页

作者： Gang Yang

In this article, I proposes an improved particle swarm hybrid butterfly optimization algorithm (IPBOA).In order to solve the problem of low accuracy and slow convergence in the butterfly optimization algorithm (BOA), I designed two strategies to improve the basic butterfly optimization algorithm (BOA) algorithm. The improved particle swarm hybrid butterfly optimization algorithm (IPBOA) includes three main steps. First, I added the cubic chaotic map to initialize the butterfly population. This has increased the diversity of butterfly populations to a certain extent; Secondly, I adopt a non-linear parameter control strategy. This strategy can effectively balance the global search and local search capabilities of the algorithm to a certain extent; finally, in order to improve the basic butterfly optimization algorithm (BOA) for global optimization, I mixed the particle swarm optimization (PSO) algorithm with BOA. In order to verify the effectiveness of the proposed algorithm, I selected 10 test functions. I use these test functions for comparative experiments. Experimental comparison results show that, compared with famous group optimization algorithms such as PSO and BOA,the performance of the IPBOA algorithm I proposed has been further improved. It has fast convergence speed, higher optimization precision and stronger robustness in the optimization problem of benchmark test functions.

关键词： Butterfly optimization algorithm Cubic Chaos Nonlinear Parameters particle swarm optimization algorithm Benchmark Test Function

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Power stabilization method based on particle swarm optimization for electric vehicle dynamic wireless charging systems

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JOURNAL OF POWER ELECTRONICS 2025年 1-12页

作者： Zhang, Ming Tao, Weiye Zhang, Hantao Huaiyin Inst Technol Fac Automat Huaian Peoples R China

In the process of the dynamic wireless charging (DWC) of an electric vehicle (EV), the relative position of the coupling coil changes, causing the problem of constant fluctuations of the charging power, which affects the service life of the battery and bring safety problems. Therefore, a power fluctuation suppression method based on the particle swarm optimization (PSO) algorithm is proposed in this paper. First, based on the LCC-S compensation topology, the transmission characteristics of an EV DWC system are analyzed. Then, a DC-DC conversion circuit is added to the rectifier and voltage regulation part of the receiver, and the double closed-loop control parameters are optimized by the PSO algorithm to achieve stable charging power for EV. Finally, the effectiveness of the proposed method is verified by simulations, and compared with the coupling mechanism design and other control strategies to solve the power fluctuation problem of EV dynamic wireless charging. Moreover, experimental results show that the double closed-loop control optimized by the PSO algorithm in this paper can restore balance within 0.08 s, and the fluctuation range is controlled to within +/- 1% when the coupling coefficient changes continuously.

关键词： Electric vehicle Dynamic wireless charging particle swarm optimization algorithm Power stabilization

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Improved Multi-Objective Strategy Diversity Chaotic particle swarm optimization of Ordered Charging Strategy for Electric Vehicles Considering User Behavior

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ENERGIES 2025年第3期18卷 690-690页

作者： Zhao, Shuyi Ma, Chenshuo Cao, Zhiao Northeastern Univ Sydney Smart Technol Coll Qinhuangdao 066004 Peoples R China Taylors Univ Sch Accounting & Finance Selangor 47500 Malaysia Natl Univ Def Technol Sch Elect Sci Changsha 410073 Peoples R China Guangdong Commun Polytech Sch Intelligent Transportat Engn Guangzhou 510800 Peoples R China

With the development of the EV industry, the number of EVs is increasing, and the random charging and discharging causes a great burden on the power grid. Meanwhile, the increasing electricity bills reduce user satisfaction. This article proposes an algorithm that considers user satisfaction to solve the charging and discharging scheduling problem of EVs. This article adds an objective function to quantify user satisfaction and addresses the issues of premature local optima and insufficient diversity in the MOPSO algorithm. Based on the performance of different particles, the algorithm assigns elite particle, general particle, and learning particle roles to the particles and assigns strategies for maintaining search, developing search, and learning search, respectively. In order to avoid falling into local optima, chaotic sequence perturbations are added during each iteration process avoiding premature falling into local optima. Finally, case studies are implemented and the comparison analysis is performed in terms of the use and benefit of each design feature of the algorithm. The results show that the proposed algorithm is capable of achieving up to 23% microgrid load reduction and up to 20% improvement in convergence speed compared to other algorithms. It is superior to other algorithms in solving the problem of orderly charging and discharging of electric vehicles and has strong usability and feasibility.

关键词： electric vehicles orderly charging and discharging tent chaotic sequence perturbation particle swarm optimization algorithm multi-objective optimization

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Image Multi-Threshold Segmentation Based on Variable Precision Rough Set and K-L Roughness particle swarm optimization

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2025年第2期32卷 704-712页

作者： She, Zhiyong Song, Tao Zhang, Dongpo Feng, Yueping Xinjiang Univ Polit Sci & Law Informat Network Secur Coll Tumxuk 844000 Xinjiang Peoples R China 52 Qianhai East St Tumushuk City Xinjiang Peoples R China Geely Univ China Sch Intelligence Technol Chengdu 610000 Sichuan Peoples R China 123 Sect 2Chengjian Ave Chengdu Sichuan Peoples R China Jilin Univ Sch Comp Sci & Technol Changchun 130012 Peoples R China 2699 Qianjin St Changchun Jilin Peoples R China

This paper proposes an image multi-threshold segmentation algorithm based on variable precision rough sets and K-L roughness particle swarm optimization. The algorithm does not require a priori knowledge outside the image and employs variable precision rough sets to address the uncertainty problem in image segmentation. The optimal segmentation threshold is obtained by combining K-L divergence and roughness, and an improved particle swarm optimization algorithm is used to enhance segmentation efficiency. Experimental results demonstrate that the proposed algorithm effectively solves the uncertainty problem in segmentation and achieves better segmentation performance compared to other algorithms.

关键词： K-L roughness divergence multi threshold segmentation particle swarm optimization algorithm variable precision rough set

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A particle swarm optimization and constraint programming-based approach for integrated process planning and scheduling with lot streaming problem

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APPLIED SOFT COMPUTING 2025年 174卷

作者： Zhang, Mengya Li, Xinyu Gao, Liang Liu, Qihao Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China

This paper studies the integrated process planning and scheduling with lot streaming (IPPS-LS) problem, which consists of lot splitting, process planning, and shop scheduling. Although the IPPS-LS problem is common in the manufacturing of flexible process products, it has not been extensively studied due to its high complexity. Hence, this study develops an enhanced particle swarm optimization algorithm based on constraint programming (CP) to minimize makespan. The proposed algorithm employs finite condition and relaxation models for particle reconfiguration and re-optimization. To achieve it, two types of relaxation models are constructed by decomposing the multiple constraints of the CP model. The algorithm dynamically updates particle encoding sequences based on model accuracy, effectively reducing invalid searches and accelerating the search process. The proposed algorithm is compared with models and other metaheuristic algorithms on 120 test instances. The impact of the relaxed CP strategy and particle swarm optimization algorithm on the proposed algorithm performance is also analyzed. Finally, a significance of difference validation is performed. Computational experiments demonstrate the efficiency of the proposed algorithm in solving the IPPS-LS problem of varying scales. In addition, the relaxed CP strategy exhibits a more significant improvement effect for medium-scale problems compared to small and large-scale problems.

关键词： Integrated process planning and scheduling Lot streaming Constraint programming particle swarm optimization algorithm Relaxation model

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CMPSO: A novel co-evolutionary multigroup particle swarm optimization for multi-mission UAVs path planning

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ADVANCED ENGINEERING INFORMATICS 2025年 63卷

作者： Hu, Gang Cheng, Mao Houssein, Essam H. Jia, Heming Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Minia Univ Fac Comp & Informat Al Minya Egypt Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China

To cope with the situation where an unmanned aerial vehicle (UVA) needs to perform missions to multiple locations, this paper presents a new multi-mission UAVs path planning model and proposes a novel co-evolutionary multigroup particle swarm optimization (CMPSO) for solving this complex model. In this model, a new ball curve, the ball lambda-Bezier curve (B lambda B), is used to represent the path of UAVs. In particular, UAV needs to satisfy G 0 and G 1 continuity at the must-pass points. Using this as a basis, a new model is built to generate a feasible path that is safe, smooth and constrained by the angle of climb and flight altitude. To solve this model efficiently, CMPSO framed by two novel different grouping learning mechanisms is proposed. Two different group learning mechanisms, grouping based on fitness values and activity level, replace the original speed and position update methods in PSO. The grouping mechanism based on the activity level uses the median of the velocity vector modes as a criterion to divide the whole population into two. They effectively facilitate the transfer of information between particles. In addition, a mutation mechanism based on the activity level is introduced to address the defect of PSO's proneness to converge to local optima. By comparing CMPSO with 15 excellent meta- heuristics at CEC 2017, CMPSO is ranked first with an average ranking of 3.72. Also, CMPSO has the best and most stable performance on 18 of the 21 engineering application problems. Finally, CMPSO is applied to three different environments of the path planning model. CMPSO outperforms the other compared algorithms in all three environments with a success rate of 100. This shows the efficiency and practicality of CMPSO in facing complex path planning problems.

关键词： UAVs path planning particle swarm optimization algorithm Group learning mechanisms

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An Adaptive Scheduling Method for Standalone Microgrids Based on Deep Q-Network and particle swarm optimization

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ENERGIES 2025年第8期18卷 2133-2133页

作者： Zhang, Borui Liu, Bo Hunan Agr Univ Coll Informat & Intelligence Changsha 410128 Peoples R China

Standalone wind-solar-diesel-storage microgrids serve as a crucial solution for achieving energy self-sufficiency in remote and off-grid areas, such as rural regions and islands, where conventional power grids are unavailable. Addressing scheduling optimization challenges arising from the intermittent nature of renewable energy generation and the uncertainty of load demand, this paper proposes an adaptive optimization scheduling method (DQN-PSO) that integrates Deep Q-Network (DQN) with particle swarm optimization (PSO). The proposed approach leverages DQN to assess the operational state of the microgrid and dynamically adjust the key parameters of PSO. Additionally, a multi-strategy switching mechanism, incorporating global search, local adjustment, and reliability enhancement, is introduced to jointly optimize both clean energy utilization and power supply reliability. Simulation results demonstrate that, under typical daily, high-volatility, and low-load scenarios, the proposed method improves clean energy utilization by 3.2%, 4.5%, and 10.9%, respectively, compared to conventional PSO algorithms while reducing power supply reliability risks to 0.70%, 1.04%, and 0.30%, respectively. These findings validate the strong adaptability of the proposed algorithm to dynamic environments. Further, a parameter sensitivity analysis underscores the significance of the dynamic adjustment mechanism.

关键词： standalone microgrids adaptive method reinforcement learning particle swarm optimization algorithm

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High performance deep violet InGaN TQW laser diode through multi-layer thickness optimization using particle swarm optimization method

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OPTICS AND LASER TECHNOLOGY 2025年 181卷

作者： Edalati, Batool Goharrizi, Azita Zandi Alahyarizadeh, Ghasem Kargaran, Hamed Grad Univ Adv Technol Dept Photon Kerman Iran Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Semicond Grp Kerman Iran Shahid Beheshti Univ Fac Engn Tehran Iran Shahed Univ Fac Engn Tehran Iran

The paper details the optimization of efficiency parameters for a InGaN TQW laser diode using the particle swarm optimization algorithm through SILVACO software. By adjusting layer thickness based on output power, slope efficiency, and threshold current, significant enhancements were achieved. Three optimized laser diodes (LD1, LD2, LD3) were compared with a primary LD, revealing notable improvements in output power and overall performance. Analysis of carrier recombination rates in the active region highlighted increased stimulated recombination and decreased non-radiative recombination, particularly in the n-side well. The study also showed a more uniform radiative recombination in the quantum wells of the optimized LDs, especially in LD3. Investigation of electron and hole concentrations, current densities, and energy levels demonstrated higher electron and hole current density in the optimized LDs, with LD3 exhibiting substantial improvements over the primary LD. Notably, the optimized LDs displayed reduced threshold current and improved slope efficiency compared to the primary LD. The study identified optimal layer thicknesses based on various cost functions, resulting in significant enhancements in output power (0.561 W), slope efficiency (2.515 W/A), and reduced threshold current (less than 0.029 A), indicative of enhanced TQW laser diode performance. Overall, the research showcases the effectiveness of the particle swarm optimization approach in optimizing GaN-based TQW laser diodes, leading to improved efficiency characteristics and demonstrating the potential for enhanced performance in practical applications.

关键词： InGaN TQW laser diode Numerical Simulation particle swarm optimization algorithm Layer thickness Performance characteristics

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