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...
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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 particleswarmoptimization (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.
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...
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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 particleswarmalgorithm. 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 particleswarmalgorithm 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.
In this article, I proposes an improved particleswarm hybrid butterfly optimizationalgorithm (IPBOA).In order to solve the problem of low accuracy and slow convergence in the butterfly optimizationalgorithm (BOA), ...
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In this article, I proposes an improved particleswarm hybrid butterfly optimizationalgorithm (IPBOA).In order to solve the problem of low accuracy and slow convergence in the butterfly optimizationalgorithm (BOA), I designed two strategies to improve the basic butterfly optimizationalgorithm (BOA) algorithm. The improved particleswarm hybrid butterfly optimizationalgorithm (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 optimizationalgorithm (BOA) for global optimization, I mixed the particleswarmoptimization (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 optimizationalgorithms 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.
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 ...
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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 particleswarmoptimization (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.
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 satisf...
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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.
This paper proposes an image multi-threshold segmentation algorithm based on variable precision rough sets and K-L roughness particleswarmoptimization. The algorithm does not require a priori knowledge outside the i...
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This paper proposes an image multi-threshold segmentation algorithm based on variable precision rough sets and K-L roughness particleswarmoptimization. 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.
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...
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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.
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 ...
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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 particleswarmoptimization (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.
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 una...
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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 particleswarmoptimization (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.
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, slo...
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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 particleswarmoptimization approach in optimizing GaN-based TQW laser diodes, leading to improved efficiency characteristics and demonstrating the potential for enhanced performance in practical applications.
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