Autonomous Evolution of High-Speed Quadruped Gaits Using Particle Swarm Optimization

2009 ◽  
pp. 259-270 ◽  
Author(s):  
Chunxia Rong ◽  
Qining Wang ◽  
Yan Huang ◽  
Guangming Xie ◽  
Long Wang
Keyword(s):  
Particle Swarm Optimization ◽  
High Speed ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Autonomous Evolution

scholarly journals A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Yan Huang ◽  
Jianhui Lin ◽  
Zechao Liu ◽  
Chenguang Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
High Speed ◽  
Fitness Function ◽  
Rapid Development ◽  
Particle Swarm ◽  
Rotation Frequency ◽  
Railway Vehicle ◽  
Swarm Optimization ◽  
Wheel Track

With the rapid development of high-speed railway, the fault diagnosis of railway vehicles has become more and more important for ensuring the operating safety. The MF is a nonlinear signal processing method which can extract the modulated faulty information via reshaping the analyzed signal. However, the choices of operators and structure elements (SE) are numerous and complicated to determine the best MF solution for different bearing faulty signals. In this paper, the particle swarm optimization (PSO) was introduced to optimize the effect of MF among several classical MF operators and different SE parameters. The proposed method applied PSO to select the best MF result with respect to the fitness function adopting kurtosis. A set of bearing signals with additional interference of wheel-track excitement are analyzed to verify the effectiveness of the proposed method. The results demonstrated that the proposed method is capable of obtaining the optimized solution and accurately extracting the fault information. Furthermore, the shaft rotation frequency and wheel-track interference were reduced by the proposed method.

Optimal design of helical flute of irregular tooth end milling cutter based on particle swarm optimization algorithm

2021 ◽  
pp. 095440622110420
Author(s):  
Haibin Yu ◽  
Minli Zheng ◽  
Wei Zhang ◽  
Wanying Nie ◽  
Tianchen Bian
Keyword(s):  
Particle Swarm Optimization ◽  
High Speed ◽  
End Milling ◽  
Dynamic Balance ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Curvature Radius ◽  
Swarm Optimization ◽  
Milling Cutter ◽  
Helical Flute

Due to the variable pitch angle and helix angle of the irregular tooth end milling cutter, the mass of the integral end milling cutter is eccentric, and the high stability and precision design of the irregular tooth end milling cutter is still a challenge. Aiming at the influence of dynamic balance of irregular tooth end milling cutter which can not be ignored in high-speed milling, the parameterized design of radial section of irregular tooth end milling cutter was carried out. Based on the space transformation law of the centroid of helical flute, a new method for calculating the centroid coordinate of end milling cutter was put forward, and a general mathematical model of eccentricity of integral end milling cutter was given. It was proved that this model could accurately calculate the centroid position and eccentricity of the end milling cutter. The influence of pitch difference angle and helix difference angle on eccentricity of end milling cutter was studied and analyzed. The particle swarm optimization (PSO) algorithm was creatively applied to optimize the helical flute shape of the end milling cutter, the curvature radius of helical flute curve is optimized, so that the centroid coordinate is infinitely close to the origin of coordinate. The number of iterations was set to 200. In the 32nd iteration, the result approached to infinitesimal, the final function converged, and obtained the groove curvature radius of the milling cuter with the smallest eccentricity. The optimized eccentricity of the end milling cutter is infinitesimal, which can make the vibration damping performance of the end milling cutter be fully developed. On the basis of ensuring the same cutting performance, the cutting tool unbalance was effectively reduced and the dynamic performance of milling cutter was further improved.

Fuzzified Swarm Intelligence Framework Using Fpsor Algorithm for High-Speed Manet

2021 ◽  
Author(s):  
Harihara Gopalan
Keyword(s):  
Particle Swarm Optimization ◽  
High Speed ◽  
Ad Hoc ◽  
Energy Levels ◽  
Research Work ◽  
Particle Swarm ◽  
Ad Hoc Routing ◽  
Swarm Optimization ◽  
Computational Overhead ◽  
Fitness Value

Abstract Ad-hoc routing protocols may be dispersed and involve every node in the route discovery process by making routing data more reliable. Mobile-Adhoc-Networks (MANETs) consist of many portable nodes that can commune directly with each other or through intermediate nodes. Repeatedly, nodes in MANETs operate with batteries and can roam freely, and thus, a node may exhaust its energy or move away without providing any notice to its cooperative nodes. In MANETs, a route consists of numerous links in sequence, and so, its lifetime is based on the lifetime of every node and also the wireless links among adjacent nodes. In this research work Fuzzified Particle Swarm Optimization oriented Routing (FPSOR) algorithm is planned to minimize data loss and computational overhead, which in turn maximizes the lifetime of MANETs. Particle Swarm Optimization oriented Routing protocol (PSOR) has taken energy competence as a significant criterion for processing routing and driving optimised path for the data accelerating and processing to the source node. The PSOR produces a fresh route for routing by considering the fitness value of energy to evaluate diverse paths and to choose the best-optimised path whose energy consumption is less as compared to ant colony optimization routing paths. This algorithm utilizes a good approach focusing energy levels/status of the nodes through fuzzification and the lengths of the routed ways. NS2 simulator is cast for performance evaluation.

Intelligent Evaluation Model for Cementing Quality Based on PSO-SVM and Application

2011 ◽  
Vol 71-78 ◽  
pp. 4293-4299 ◽  
Author(s):  
Jing Cheng Liu ◽  
Hong Tu Wang ◽  
Shun Peng Zeng ◽  
Zhi Gang Yuan
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
High Speed ◽  
Evaluation Model ◽  
Research Result ◽  
Particle Swarm ◽  
Normal Operation ◽  
Support Vector ◽  
Swarm Optimization ◽  
Gas Well

The cementing quality is directly related to the normal operation of the gas well, therefore, the evaluation of cementing quality is key to the correctly use the gas well as well as to take measures to protect the gas well. In this paper, four first wave amplitudes at the same depth point when using the borehole compensated sonic logger with double transceiver technique to carry out the acoustic amplitude log operation are served as the discriminant factors to evaluate the cementing quality. Taking the engineering actual measured data as the learning samples and using the particle swarm optimization to optimize the parameters of support vector machine, this paper established the intelligent evaluation model for cementing quality based on particle swarm optimization (PSO) and support vector machine (SVM). The model employs the excellent characteristic of SVM which has high speed of solving and could describe nonlinear relation as well as the characteristic of PSO which has global optimization. Through test of engineering samples, the research result showed that this model has fast astringency and high precision, providing a new method and approach for the fast and accurate evaluation of the well cementing quality.

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