A simplified recombinant PSO

Authors:
Dan Bratton;Tim Blackwell
Affiliations:
Department of Computing, Goldsmiths, University of London, New Cross, London, UK;Department of Computing, Goldsmiths, University of London, New Cross, London, UK
Venue:
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
Year:
2008

Citing 4
Cited 6

Exposing origin-seeking bias in PSO

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Particle Swarm Optimization with Discrete Recombination: An Online Optimizer for Evolvable Hardware

AHS '06 Proceedings of the first NASA/ESA conference on Adaptive Hardware and Systems
Origin of bursts

Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
The fully informed particle swarm: simpler, maybe better

IEEE Transactions on Evolutionary Computation

Examination of particle tails

Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
Simple Dynamic Particle Swarms without Velocity

ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Real-time particle swarm optimization based parameter identification applied to permanent magnet synchronous machine

Applied Soft Computing
Hybrid particle swarm optimisation based on history information sharing

Proceedings of the 13th annual conference on Genetic and evolutionary computation
A hybrid particle swarm optimisation with differential evolution approach to image segmentation

EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Improved Particle Swarm Optimization Algorithm for Android Medical Care IOT using Modified Parameters

Journal of Medical Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Simplified forms of the particle swarm algorithm are very beneficial in contributing to understanding how a particle swarm optimization (PSO) swarm functions. One of these forms, PSO with discrete recombination, is extended and analyzed, demonstrating not just improvements in performance relative to a standard PSO algorithm, but also significantly different behavior, namely, a reduction in bursting patterns due to the removal of stochastic components from the update equations.