A Comparison Between SPSO and QPSO from View Point of Optimization

Teaba Wala Aldeen Khairi ,Wisam Ali Mahmod ,Mohammed Sabah Rasheed

doi:10.24237/djps.15.03.497c

Abstract

A Comparison Between SPSO and QPSO from View Point of Optimization

Highlights

Swarm intelligence (SI) is the combined attitude of decentralized, natural or artificial, selforganized systems
The fundamental Particle swarm optimization (PSO) [5] model contains a swarm of particles, which are started with a population of stochastic nominee solutions
In the quantum behavior of particle swarm optimization (QPSO) algorithm, the state of a particle is drawn by wave function Ψ (x, t) instead of velocity and position

Summary

Introduction

Swarm intelligence (SI) is the combined attitude of decentralized, natural or artificial, selforganized systems. The fundamental PSO [5] model contains a swarm of particles, which are started with a population of stochastic nominee solutions. They proceed iteratively through the problem space of d-dimensions to search for better solutions. The mean best position was introduced into the iterative equation of QPSO to enhance the global search ability of the particle [34]. In the QPSO algorithm, the state of a particle is drawn by wave function Ψ (x, t) instead of velocity and position. Initialize the swarm Begin While the condition termination not met Do Calculate mbest by equation (3) Update particle positions by using equation (1) Update pbest Update gbest End do End

Benchmark Test Functions

Discussion

Conclusion