2009
DOI: 10.1504/ijor.2009.026534
|View full text |Cite
|
Sign up to set email alerts
|

Particle Swarm Optimization algorithm with multiple social learning structures

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
28
0

Year Published

2009
2009
2020
2020

Publication Types

Select...
6
3

Relationship

3
6

Authors

Journals

citations
Cited by 48 publications
(28 citation statements)
references
References 14 publications
0
28
0
Order By: Relevance
“…However, no research literature is found that addresses this issue. Pongchairerks and Kachitvichyanukul (2005) proposed a use of heterogeneous population in PSO to allow some fraction of the swarm to move by crossover with the best particle.…”
Section: The Effect Of Sub-groupingmentioning
confidence: 99%
“…However, no research literature is found that addresses this issue. Pongchairerks and Kachitvichyanukul (2005) proposed a use of heterogeneous population in PSO to allow some fraction of the swarm to move by crossover with the best particle.…”
Section: The Effect Of Sub-groupingmentioning
confidence: 99%
“…Several approaches have been proposed to deal with this problem. One of the approaches, introduced by Pongchairerks and Kachitvichyanukul (2009b), utilizes multiple social learning terms and extends the concept of the standard PSO in GLNPSO. Instead of using the global best particle only as reference, GLNPSO also incorporates the local best and near-neighbor best (Veeramachaneni et al, 2003) as additional social learning reference terms.…”
Section: Glnpsomentioning
confidence: 99%
“…The GLN-PSO algorithm is an extension of the standard PSO algorithm using the global, local, and near neighbour best positions simultaneously in order to update the particle velocities 10,11 . It outperforms the standard version in terms of solution quality.…”
Section: Description Of Gln-pso C Algorithmmentioning
confidence: 99%
“…There are many variants of the PSO algorithm in the literature [9][10][11][12] . We will use the GLN-PSO c algorithm 10,11 since it enables the swarm to explore different parts of the search spaces simultaneously. This algorithm is an extension of the GLN-PSO algorithm, some solutions of which are generated via the crossover operator.…”
Section: Introductionmentioning
confidence: 99%