Optimal volume design of planetary gear train using particle swarm optimization

Particle swarm optimization algorithms have lots of advantages such as fast convergence speed, good quality of solution and robustness in multidimensional space function optimization and dynamic target optimization. It is suitable for structural optimization design. In this paper, manual transmission gear train of a tractor is taken as research object, the minimum quality and minimum center distance of the gear train is taken as optimization goal, the gear ratio, modulus, helix angle, tooth width and equilibrium conditions of the axial force are taken as the constraints, a multi-objective optimization model of the gear train is established. The optimal structure design programs and Pareto optimal solution are obtained by using particle swarm optimization algorithm.

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Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms

Mechanism and Machine Theory ◽

10.1016/j.mechmachtheory.2009.10.010 ◽

2010 ◽

Vol 45 (3) ◽

pp. 531-541 ◽

Cited By ~ 83

Author(s):

V. Savsani ◽

R.V. Rao ◽

D.P. Vakharia

Keyword(s):

Particle Swarm Optimization ◽

Simulated Annealing ◽

Particle Swarm ◽

Gear Train ◽

Swarm Optimization ◽

Optimal Weight ◽

Weight Design ◽

Annealing Algorithms

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Feasibility enhanced particle swarm optimization for constrained mechanical design problems

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406216681593 ◽

2016 ◽

Vol 232 (2) ◽

pp. 381-400 ◽

Cited By ~ 5

Author(s):

Mehmet Sinan Hasanoglu ◽

Melik Dolen

Keyword(s):

Particle Swarm Optimization ◽

Constraint Satisfaction ◽

Optimization Problems ◽

Particle Swarm ◽

Search Space ◽

Gear Train ◽

Satisfaction Level ◽

Objective Functions ◽

Swarm Optimization ◽

Enhanced Particle Swarm Optimization

Constrained optimization problems constitute an important fraction of optimization problems in the mechanical engineering domain. It is not uncommon for these problems to be highly-constrained where a specialized approach that aims to improve constraint satisfaction level of the whole population as well as finding the optimum is deemed useful especially when the objective functions are very costly. A new algorithm called Feasibility Enhanced Particle Swarm Optimization (FEPSO), which treats feasible and infeasible particles differently, is introduced. Infeasible particles in FEPSO do not need to evaluate objective functions and fly only based on social attraction depending on a single violated constraint, called the activated constraint, which is selected at each iteration based on constraint priorities and flight occurs only along dimensions of the search space to which the activated constraint is sensitive. To ensure progressive improvement of constraint satisfaction, particles are not allowed to violate a satisfied constraint in FEPSO. The highly-constrained four-stage gear train problem and its two variants introduced in this paper are used to assess the effectiveness of FEPSO. The results suggest that FEPSO is effective and consistent in obtaining feasible points, finding good solutions, and improving the constraint satisfaction level of the swarm as a whole.

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Particle Swarm Optimization and Real-Road/Driving-Cycle Analysis Based Powertrain System Design for Dual Motor Coupling Electric Vehicle

World Electric Vehicle Journal ◽

10.3390/wevj11040069 ◽

2020 ◽

Vol 11 (4) ◽

pp. 69

Author(s):

Chao Ma ◽

Shiwei Jin ◽

Kun Yang ◽

Di Tan ◽

Jie Gao ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Electric Vehicle ◽

High Efficiency ◽

Particle Swarm ◽

Planetary Gear ◽

Driving Cycle ◽

Operating Efficiency ◽

System Efficiency ◽

Swarm Optimization ◽

Driving Conditions

In this study, a planetary gear based dual motor coupling electric vehicle is proposed, which achieves higher system efficiency by enabling motor working under high operating efficiency area. Firstly, the dynamic characteristics of the proposed configuration are analyzed and the reasonable working modes are established. Secondly, the optimal dual motor parameters are derived according to the statistical analysis on the typical driving conditions and the collected real road driving data. Especially, the optimal parameters of planetary gear and final transmission ratio are obtained using particle swarm optimization algorithm. Finally, based on the developed mode shift algorithm, the dual motor coupling full vehicle model is developed and the vehicle performance is analyzed using MATLAB/Simulink. For the UDDS (Urban Dynamometer Driving Schedule) driving cycle, it is seen from the simulation results of motor operating points that the proposed dual motor configuration is mostly operated under the high efficiency range, and the power consumption is significantly reduced by 7.6% compared with the single motor configuration. For the NEDC (New European Driving Cycle), WLTC (Worldwide Harmonized Light Vehicles Test Cycle) and real road driving conditions, the proposed dual motor configuration also achieves system efficiency improvement of 5.0%~16.3%, which confirms the validity of the proposed configuration and its corresponding parameter matching and control algorithm development.

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Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189048 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5699-5711

Author(s):

Shirong Long ◽

Xuekong Zhao

Keyword(s):

Feature Extraction ◽

Particle Swarm Optimization ◽

Deep Learning ◽

Real Time ◽

Image Recognition ◽

Particle Swarm ◽

Learning Technology ◽

Search Performance ◽

Swarm Optimization ◽

Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan ◽

10.32492/jintake.v9i1.720 ◽

2018 ◽

Vol 9 (1) ◽

pp. 1-5

Author(s):

Fachrudin Hunaini ◽

Imam Robandi ◽

Nyoman Sutantra

Keyword(s):

Fuzzy Logic ◽

Particle Swarm Optimization ◽

Control System ◽

Membership Function ◽

Fuzzy Logic Control ◽

Particle Swarm ◽

Optimization Method ◽

Swarm Optimization ◽

Motion Error ◽

Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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