Optimization of MSA with Swift Particle Swarm Optimization

Abstract

In this Paper a new designing method is proposed for Circular Patch Micro strip Antenna (MSA) using an artificial search Algorithm named Particle swarm Optimization (PSO). It needs two stages for designing. Firstly circular patch MSA antenna needs modelling using some benchmark function. Then in second stage it's require inverse modelling using an artificial search algorithm (PSO) with some constraints. According to above steps first bandwidth of MSA is modelled using bench mark function as an input and resulted output are in form of frequency range, circular patch radius (r), ground plane length, substrate thickness, electrical thickness and dielectric loss tangent using Artificial search method. This paper presents the strategy that at the starting process cognition-learning random factor has more effect then social learning random factor. Gradually social learning random factor has more impact after learning cognition random factor to find out global best. The aim is to find out under above circumstances these modifications in PSO (Swift PSO) can give better result for optimization of Micro Strip Antenna (MSA).

Entropy based Binary Particle Swarm Optimization and classification for ear detection

“…Eq. (6) gives the formula for velocity; it is very similar to the PSO velocity update equation suggested by Rathi et al (2010). The only change is that a uniform random variable rand is multiplied to the previous velocity v t in order to reduce the rate of convergence of the particles.…”