Hybrid particle swarm and conjugate gradient optimization algorithm

Authors:
Abdallah Qteish;Mohammad Hamdan
Affiliations:
Department of Physics, Yarmouk University, Irbid, Jordan;Department of Computer Science, Yarmouk University, Irbid, Jordan
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2010

Citing 6
Cited 0

Swarm intelligence

Swarm intelligence
Dynamic Search With Charged Swarms

GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Breeding swarms: a GA/PSO hybrid

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Multiobjective optimization using dynamic neighborhood particle swarm optimization

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A note on the empirical evaluation of intermediate recombination

Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work we propose a different particle swarm optimization (PSO) algorithm that employs two key features of the conjugate gradient (CG) method Namely, adaptive weight factor for each particle and iteration number (calculated as in the CG approach), and periodic restart Experimental results for four well known test problems have showed the superiority of the new PSO-CG approach, compared with the classical PSO algorithm, in terms of convergence speed and quality of obtained solutions.