scholarly journals A new technique to design planar dipole antennas by using Bezier curve and Particle Swarm Optimization

2016 ◽  
Vol 65 (3) ◽  
pp. 513-525 ◽  
Author(s):  
Nuttaka Homsup ◽  
Winyou Silabut ◽  
Vuttichai Kesornpatumanum ◽  
Pravit Boonek ◽  
Waroth Kuhirun
Keyword(s):  
Particle Swarm Optimization ◽  
Ieee 802.11 ◽  
Particle Swarm ◽  
New Technique ◽  
Dual Band ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Swarm Optimization ◽  
A New Technique ◽  
5 Ghz

Abstract This research presents a new technique which includes the principle of a Bezier curve and Particle Swarm Optimization (PSO) together, in order to design the planar dipole antenna for the two different targets. This technique can improve the characteristics of the antennas by modifying copper textures on the antennas with a Bezier curve. However, the time to process an algorithm will be increased due to the expansion of the solution space in optimization process. So as to solve this problem, the suitable initial parameters need to be set. Therefore this research initialized parameters with reference antenna parameters (a reference antenna operates on 2.4 GHz for IEEE 802.11 b/g/n WLAN standards) which resulted in the proposed designs, rapidly converted into the goals. The goal of the first design is to reduce the size of the antenna. As a result, the first antenna is reduced in the substrate size from areas of 5850 mm2 to 2987 mm2 (48.93% approximately) and can also operates at 2.4 GHz (2.37 GHz to 2.51 GHz). The antenna with dual band application is presented in the second design. The second antenna is operated at 2.4 GHz (2.40 GHz to 2.49 GHz) and 5 GHz (5.10 GHz to 5.45 GHz) for IEEE 802.11 a/b/g/n WLAN standards.

Particle Swarm Optimization Algorithms Applied to Antenna and Microwave Design Problems

2012 ◽  
pp. 100-126
Author(s):  
Sotirios K. Goudos ◽  
Zaharias D. Zaharis ◽  
Konstantinos B. Baltzis
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Dual Band ◽  
Design Problems ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Fitness Sharing ◽  
Microwave Design ◽  
Velocity Mutation

Particle Swarm Optimization (PSO) is an evolutionary optimization algorithm inspired by the social behavior of birds flocking and fish schooling. Numerous PSO variants have been proposed in the literature for addressing different problem types. In this chapter, the authors apply different PSO variants to common antenna and microwave design problems. The Inertia Weight PSO (IWPSO), the Constriction Factor PSO (CFPSO), and the Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithms are applied to real-valued optimization problems. Correspondingly, discrete PSO optimizers such as the binary PSO (binPSO) and the Boolean PSO with velocity mutation (BPSO-vm) are used to solve discrete-valued optimization problems. In case of a multi-objective optimization problem, the authors apply two multi-objective PSO variants. Namely, these are the Multi-Objective PSO (MOPSO) and the Multi-Objective PSO with Fitness Sharing (MOPSO-fs) algorithms. The design examples presented here include microwave absorber design, linear array synthesis, patch antenna design, and dual-band base station antenna optimization. The conclusion and a discussion on future trends complete the chapter.

scholarly journals 5GHz MMIC LNA Design Using Particle Swarm Optimization

2013 ◽  
Vol 5 (6) ◽  
pp. 257-262
Author(s):  
Low Wen Shin ◽  
Arjuna Marzuki .
Keyword(s):  
Particle Swarm Optimization ◽  
Integrated Circuit ◽  
Noise Figure ◽  
Particle Swarm ◽  
Low Noise ◽  
Design System ◽  
Stability Factor ◽  
Power Gain ◽  
Swarm Optimization ◽  
5 Ghz

This research presents an optimization study of a 5 GHz Monolithic Microwave Integrated Circuit (MMIC) design using Particle Swarm Optimization (PSO). MMIC Low Noise Amplifier (LNA) is a type of integrated circuit device used to capture signal operating in the microwave frequency. This project consists of two stages: implementation of PSO using MATLAB and simulation of MMIC design using Advanced Design System (ADS). PSO model that mimics the biological swarm behavior is developed to optimize the MMIC design variables in order to achieve the required circuit performance and specifications such as power gain, noise figure, drain current and circuit stability factor. Simulation results show that the proposed MMIC design fulfills the circuit stability factor and achieves a power gain of 19.73dB, a noise figure of 1.15 dB and a current of 0.0467A.

scholarly journals Obstacle avoidance and multitarget tracking of a super redundant modular manipulator based on Bezier curve and particle swarm optimization

2020 ◽  
Author(s):  
Chen Li ◽  
Ying Ma ◽  
Yu Zhang ◽  
Jinguo Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Obstacle Avoidance ◽  
Trajectory Tracking ◽  
Degrees Of Freedom ◽  
Optimization Method ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Redundant Manipulator ◽  
Swarm Optimization ◽  
Composite Optimization

Abstract A super redundant serpentine manipulator has slender structure and multiple degrees of freedom and can travel through narrow space and move in complex space. This manipulator is composed of many modules that can form different lengths of robot arms for different application sites. The increase in degrees of freedom causes the inverse kinematics of redundant manipulator to be typical and immensely increases the calculation load in the joint space. This paper presents a composite optimization method of path planning for obstacle avoidance and discrete trajectory tracking of a super redundant manipulator. In this composite optimization, path planning is established on a Bezier curve, particle swarm optimization is adopted to adjust the control points of the Bezier curve with the kinematic constraints of manipulator, and a feasible obstacle avoidance path is obtained along with a discrete trajectory tracking using a follow-the-leader strategy. The relative distance between each two discrete path points is limited to reduce the fitting error of the connecting rigid links to the smooth curve. Simulation results show that this composite optimization method can rapidly search for the appropriate trajectory to guide the manipulator in obtaining the target while achieving obstacle avoidance and meeting joint constraints. The proposed algorithm is suitable for 3D space obstacle avoidance and multitarget path tracking.

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