Analysis and Design of E-Shaped Dual-Frequency Microstrip Antenna Based on CPSO Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 487-491
Author(s):  
Wei Ao ◽  
Wan Qin Xiang ◽  
Chun Ming Chen ◽  
Wei Tian ◽  
De Bin Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Microstrip Antenna ◽  
Dual Frequency ◽  
Wide Bandwidth ◽  
Swarm Optimization ◽  
Design And Optimization ◽  
Analysis And Design ◽  
Optimized Model ◽  
Simulation Results ◽  
Chaotic Particle Swarm Optimization

In order to solve the problem of design and optimization for E-shaped patch microstrip antenna, chaotic particle swarm optimization (CPSO) algorithm was proposed to assist the procedure. First, the E-shaped antennas model was created, and then, the parameters of this antenna were adjusted according to the CPSO algorithm, and the procedure was repeated until the antennas capabilities meet the requirements. The simulation results showed that, the optimized model of such antenna, was capable of dual-frequency operation in 1.8GHz and 2.4GHz, and it was also capable of wide bandwidth, could meet the requirements very well.

Microwave Circuit Design

2012 ◽  
pp. 18-39
Author(s):  
Gabriel Cormier ◽  
Tyler Ross
Keyword(s):  
Particle Swarm Optimization ◽  
Global Optimum ◽  
Model Parameters ◽  
Microwave Circuit ◽  
Small Signal ◽  
Signal Model ◽  
Swarm Optimization ◽  
High Frequencies ◽  
Design And Optimization ◽  
Simulation Results

Circuit models play an important role in the design and optimization of microwave circuits (circuits in the GHz frequency range). These circuit models contain many parameters, including parasitic elements, necessary to correctly model the behavior of transistors at high frequencies. These models are often designed based on a series of measurements. Because of its ability to efficiently locate the global optimum of an objective function, particle swarm optimization (PSO) can be a useful tool when matching a model to its measurements. In this chapter, PSO will be used to calculate a transistor’s small-signal model parameters, determine the noise parameters of the transistor, and design a microwave mixer. The mixer is designed at 39.25 GHz, and a comparison between measurements and simulation results shows good agreement.

scholarly journals A chaotic particle swarm optimization algorithm for solving optimal power system problem of electric vehicle

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983350 ◽  
Author(s):  
Tianjun Zhu ◽  
Hongyan Zheng ◽  
Zonghao Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Electric Vehicles ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Vehicle Dynamic ◽  
Swarm Optimization ◽  
Simulation Results ◽  
Chaotic Particle Swarm Optimization

Transportation of electrification has become a hot issue in recent decades and the large-scale deployment of electric vehicles has yet to be actualized. This article proposes a powertrain parameter optimization design approach based on chaotic particle swarm optimization algorithm. To improve the driving and economy performance of pure electric vehicles, chaotic particle swarm optimization algorithm is adopted in this study to optimize principal parameters of vehicle power system. Vehicle dynamic performance simulations were carried out in the Cruise software, and the simulation results before and after optimization were compared. Simulation results show that optimized vehicles by chaotic particle swarm optimization can meet the expected dynamic performance and the driving range has been greatly improved. Meanwhile, it is also viable that the parameters of the optimal objective function can achieve the purpose of balancing the driving performance and economic performance, which provides a reference for the development of vehicle dynamic performance.

scholarly journals Particle Swarm Optimization Algorithm with Adaptive Chaos Perturbation

2015 ◽  
Vol 15 (6) ◽  
pp. 70-80 ◽  
Author(s):  
Dong Yong ◽  
Wu Chuansheng ◽  
Guo Haimin
Keyword(s):  
Numerical Simulation ◽  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Chaotic Sequence ◽  
Swarm Optimization ◽  
Chaotic Mapping ◽  
Simulation Results ◽  
Chaotic Particle Swarm Optimization ◽  
Modified Algorithm

Abstract Most of the existing chaotic particle swarm optimization algorithms use logistic chaotic mapping. However, the chaotic sequence which is generated by the logistic chaotic mapping is not uniform enough. As a solution to this defect, this paper introduces the Anderson chaotic mapping to the chaotic particle swarm optimization, using it to initialize the position and velocity of the particle swarm. It self-adaptively controls the portion of particles to undergo chaos update through a change of the fitness variance. The numerical simulation results show that the convergence and global searching capability of the modified algorithm have been improved with the introduction of this mapping and it can efficiently avoid premature convergence.

Study on Vehicle Logistics Routing Path Problem with Time Windows Based on Particle Swarm Optimization

2014 ◽  
Vol 687-691 ◽  
pp. 5161-5164
Author(s):  
Lian Zhou Gao
Keyword(s):  
Particle Swarm Optimization ◽  
World Economy ◽  
Time Window ◽  
Time Windows ◽  
Particle Swarm ◽  
Modern Logistics ◽  
Swarm Optimization ◽  
Logistics Industry ◽  
Search Speed ◽  
Simulation Results

As the development of world economy, how to realize the reasonable vehicle logistics routing path problem with time window constrain is the key issue in promoting the prosperity and development of modern logistics industry. Through the research of vehicle logistics routing path 's demand, particle swarm optimization with a novel particle presentation is designed to solve the problem which is improved, effective and adept to the normal vehicle logistics routing. The simulation results of example indicate that the algorithm has more search speed and stronger optimization ability.

Precision Motion of Iterative Learning Controller Using Adaptive Filter Bandwidth Tuning by Improved Particle Swarm Optimization Technique

2013 ◽  
Vol 376 ◽  
pp. 349-353
Author(s):  
Yi Cheng Huang ◽  
Shu Ting Li ◽  
Kuan Heng Peng
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Iterative Learning ◽  
Swarm Optimization ◽  
Filter Bandwidth ◽  
Improved Particle Swarm Optimization ◽  
Simulation Results ◽  
Adaptive Bandwidth ◽  
Precision Motion ◽  
Zero Phase

This paper utilized the Improved Particle Swarm Optimization (IPSO) technique for adjusting the gains of PID and the bandwidth of zero-phase Butterworth Filter of an Iterative Learning Controller (ILC) for precision motion. Simulation results show that IPSO-ILC-PID controller without adaptive bandwidth filter tuning have the chance of producing high frequencies in the error signals when the filter bandwidth is fixed for every repetition. However the learnable and unlearnable error signals should be separated for bettering control process. Thus the adaptive bandwidth of a zero phase filter in ILC-PID controller with IPSO tuning is applied to one single motion axis of a CNC table machine. Simulation results show that the developed controller can cancel the errors efficiently as repetition goes. The frequency response of the error signals is analyzed by the empirical mode decomposition (EMD) and the Hilbert-Huang Transform (HHT) method. Errors are reduced and validated by ILC with adaptive bandwidth filtering design.

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