An augmented particle swarm model based bi-acceleration factor

Purpose - Particle swarm optimization (PSO) has been applied with success to many numerical and combinatorial optimization problems in recent years. However, a great deal of work remains to be done to improve the particle swarm performance. The purpose of this paper is to present a new adaptive PSO...

Full description

Saved in:
Bibliographic Details
Published in:International journal of intelligent computing and cybernetics 2011-06, Vol.4 (2), p.187-205
Main Author: Mohamed Ben Ali, Yamina
Format: Article
Language:English
Subjects:
Adaptation
Algorithms
Benchmarking
Combinatorial analysis
Genetic algorithms
Inertia
Mathematical analysis
Mathematical models
Optimization
Optimization techniques
Performance enhancement
Population
Studies
Velocity
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Purpose - Particle swarm optimization (PSO) has been applied with success to many numerical and combinatorial optimization problems in recent years. However, a great deal of work remains to be done to improve the particle swarm performance. The purpose of this paper is to present a new adaptive PSO approach to overcome convergence drawbacks. Thus, the updating of the particle position rule and the introduction of new acceleration parameter augment the performance of the proposed model developed in this perspective.Design methodology approach - In the studied picture, each particle defined in a multidimensional search space is represented by a vector of three adaptive parameters representing, respectively, the adaptive cognitive factor, the adaptive social factor, and the bi-acceleration factor. Therefore, to updating its position rule, the authors add a gaussian noise to each updated velocity in order to increase the diversity in the population swarm.Findings - The simulation experiments uses the CEC, 2005 functions benchmark. The achieved results show that the proposed model improves the existing performance of other algorithms compared to the same benchmark.Originality value - The proposed algorithm improves the performance of the PSO based on the self-adaptation strategy. Thus, it can actually resolve hard functions which introduces noisy and shifted functions.
ISSN:1756-378X
1756-3798
DOI:10.1108/17563781111136694