Particle swarm optimization procedure in determining parameters in Chaboche kinematic hardening model to assess ratcheting under uniaxial and biaxial loading cycles

2018 ◽  
Vol 41 (7) ◽  
pp. 1637-1645 ◽  
Author(s):  
Jiawen Li ◽  
Qiang Li ◽  
Jinpeng Jiang ◽  
Jian Dai
Keyword(s):  
Particle Swarm Optimization ◽  
Biaxial Loading ◽  
Kinematic Hardening ◽  
Particle Swarm ◽  
Optimization Procedure ◽  
Swarm Optimization ◽  
Hardening Model ◽  
Uniaxial And Biaxial Loading

scholarly journals Optimierung von FSS-Bandpassfiltern mit Hilfe der Schwarmintelligenz (Particle Swarm Optimization)

2006 ◽  
Vol 4 ◽  
pp. 65-71 ◽  
Author(s):  
G. Wu ◽  
V. Hansen ◽  
E. Kreysa ◽  
H.-P. Gemünd
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Frequency Selective Surfaces ◽  
Transmission Characteristics ◽  
Complete Structure ◽  
Swarm Optimization ◽  
Band Pass ◽  
Linear Problems

Abstract. In diesem Beitrag wird ein neues Verfahren zur Optimierung von Bandpassfiltern aus mehrlagigen frequenzselektiven Schirmen (FSS), die in ein Dielektrikum eingebettet sind, vorgestellt. Das Ziel ist es, die Parameter der gesamten Struktur so zu optimieren, dass ihre Transmissionseigenschaften hohe Filteranforderungen erfüllen. Als Optimierungsverfahren wird die Particle Swarm Optimization (PSO) eingesetzt. PSO ist eine neue stochastische Optimierungsmethode, die in verschieden Gebieten, besonders aber bei der Optimierung nicht linearer Probleme mit mehreren Zielfunktionen erfolgreich eingesetzt wird. In dieser Arbeit wird die PSO in die Spektralbereichsanalyse zur Berechnung komplexer FSS-Strukturen integriert. Die numerische Berechnung basiert auf einer Integralgleichungsformulierung mit Hilfe der spektralen Greenschen Funktion für geschichtete Strukturen. This paper presents a novel procedure for the optimization of band-pass filters consisting of frequency selective surfaces (FSS) embedded in a dielectric. The aim is to optimize the parameters of the complete structure so that the transmission characteristics of the filters fulfill the demanding requirements. The Particle Swarm Optimization (PSO) is used as the optimization procedure. PSO is a new stochastic optimization method that is successfully applied in different areas for the optimization of non-linear problems with several object-functions. In this work, PSO is integrated into the spectral domain analysis for the calculation of the complex FSS structures. The numerical computation is based on the formulation of an integral equation with the help of the spectral Green's function for layered media.

scholarly journals Feasible Optimal Solutions of Electromagnetic Cloaking Problems by Chaotic Accelerated Particle Swarm Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2725
Author(s):  
Alkmini Michaloglou ◽  
Nikolaos L. Tsitsas
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Optimization Procedure ◽  
Optimal Designs ◽  
Swarm Optimization ◽  
The Core ◽  
Low Profile ◽  
Accelerated Particle Swarm Optimization ◽  
Spherical Core

The optimization problem of cloaking a perfectly electric conducting or dielectric spherical core is investigated. The primary excitation is due to an external magnetic dipole. The chaotic accelerated particle swarm optimization (CAPSO) algorithm is adjusted and applied to this optimization problem. The optimization variables are the radii, the permittivities and the permeabilities of a small number of spherical shells covering the core. Several feasible optimal designs are obtained, which exhibit perfect or almost perfect cloaking performance for all angles of observation. These optimal designs correspond to two, three or four spherical coating layers composed of ordinary materials. Detailed parametric investigations of the cloaking mechanism with respect to the type and radius of the core and the location of the primary dipole are carried out. The presented optimization procedure and the reported results are expected to be useful in applications like scattering and characterization of optical particles as well as in designing low-profile receiving antennas.

Optimum Design of Hybrid EDFA/FRA by Particle Swarm Optimization

2012 ◽  
pp. 148-168
Author(s):  
Alireza Mowla ◽  
Nosrat Granpayeh ◽  
Azadeh Rastegari Hormozi
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Procedure ◽  
Fiber Amplifier ◽  
Optimization Method ◽  
Fiber Amplifiers ◽  
Swarm Optimization ◽  
Erbium Doped Fiber Amplifier ◽  
Fiber Raman Amplifier ◽  
Erbium Doped

In this chapter, the authors introduce the hybrid erbium-doped fiber amplifier (EDFA)/fiber Raman amplifier (FRA) and its optimization procedure by particle swarm optimization (PSO). EDFAs, FRAs, and their combinations, which have the advantages of both, are the most important optical fiber amplifiers that overcome the signal power attenuations in the long-haul communication. After choosing a proper configuration for a hybrid EDFA/FRA, users have to choose its numerous parameters such as the lengths, pump powers, number and wavelengths of pumps, number of signal channels and their wavelengths, the signal input powers, the kind of the fibers and their characteristics such as the radius of the core, numerical apertures, and the density of Er3+ ions in the EDFA. As can be seen, there are many parameters that need to be chosen properly. Here, efficient heuristic optimization method of PSO is used to solve this problem.

Application of a PSO algorithm for identification of the parameters of Jiles-Atherton hysteresis model

2012 ◽  
Vol 61 (2) ◽  
pp. 139-148 ◽  
Author(s):  
Łukasz Knypiński ◽  
Lech Nowak ◽  
Piotr Sujka ◽  
Kazimierz Radziuk
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Procedure ◽  
Computer Code ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Hysteresis Model ◽  
Swarm Optimization ◽  
Design Variables ◽  
Particle Swarm Optimization Method

Application of a PSO algorithm for identification of the parameters of Jiles-Atherton hysteresis modelIn the paper an algorithm and computer code for the identification of the hysteresis parameters of the Jiles-Atherton model have been presented. For the identification the particle swarm optimization method (PSO) has been applied. In the optimization procedure five design variables has been assumed. The computer code has been elaborated using Delphi environment. Three types of material have been examined. The results of optimization have been compared to experimental ones. Selected results of the calculation for different material are presented and discussed.

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

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.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

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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