2016
DOI: 10.1007/978-3-319-19635-0
|View full text |Cite
|
Sign up to set email alerts
|

Fractional Order Darwinian Particle Swarm Optimization

Abstract: SpringerBriefs in Applied Sciences and TechnologyMore informations about this series at

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
29
0

Year Published

2019
2019
2022
2022

Publication Types

Select...
4
2

Relationship

1
5

Authors

Journals

citations
Cited by 56 publications
(30 citation statements)
references
References 42 publications
0
29
0
Order By: Relevance
“…The PSO basically takes advantage of the swarm intelligence concept, which is the property of a system whereby the collective behaviors of unsophisticated agents that are interacting locally with their environment, create coherent global functional patterns. More recently, and based on the concepts inherent to the PSO, the DPSO [36] and the FOPSO [37], an extended version denoted as FODPSO was presented in [35], in which several swarms compete using Darwin's survival-of-the-fittest principles and fractional calculus to control the convergence rate of the algorithm. Using those principles, the FODPSO enhances the ability of the PSO algorithm to escape from local optima by running several simultaneous parallel PSO algorithms, each being a different swarm, on the same test problem and apply a simple selection mechanism.…”
Section: B General Approachmentioning
confidence: 99%
See 4 more Smart Citations
“…The PSO basically takes advantage of the swarm intelligence concept, which is the property of a system whereby the collective behaviors of unsophisticated agents that are interacting locally with their environment, create coherent global functional patterns. More recently, and based on the concepts inherent to the PSO, the DPSO [36] and the FOPSO [37], an extended version denoted as FODPSO was presented in [35], in which several swarms compete using Darwin's survival-of-the-fittest principles and fractional calculus to control the convergence rate of the algorithm. Using those principles, the FODPSO enhances the ability of the PSO algorithm to escape from local optima by running several simultaneous parallel PSO algorithms, each being a different swarm, on the same test problem and apply a simple selection mechanism.…”
Section: B General Approachmentioning
confidence: 99%
“…In the DPSO, multiple swarms of test solutions performing just like an ordinary PSO may exist at any time with rules governing the collection of swarms that are designed to simulate natural selection. More recently, Couceiro et al [35] further extended the DPSO using fractional calculus to control the convergence rate of the algorithm. In [35], fractional-order DPSO (FODPSO) was successfully compared to both the fractional-order PSO (FOPSO) from Pires et al [37] and the traditional DPSO.…”
Section: Introductionmentioning
confidence: 99%
See 3 more Smart Citations