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Fractional order Darwinian particle ...

Couceiro, Micael.

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Fractional order Darwinian particle swarm optimization = applications and evaluation of an evolutionary algorithm /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fractional order Darwinian particle swarm optimization/ by Micael Couceiro, Pedram Ghamisi.
Reminder of title:	applications and evaluation of an evolutionary algorithm /
Author:	Couceiro, Micael.
other author:	Ghamisi, Pedram.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	x, 75 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Particle Swarm Optimization (PSO) -- Fractional Order Darwinian PSO (FODPSO) -- Case Study I: Curve Fitting -- Case Study II: Image Segmentation -- Case Study III: Swarm Robotics -- Conclusions.
Contained By:	Springer eBooks
Subject:	Swarm intelligence. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-19635-0
ISBN:	9783319196350$q(electronic bk.)

Fractional order Darwinian particle swarm optimization = applications and evaluation of an evolutionary algorithm /
Couceiro, Micael.

Fractional order Darwinian particle swarm optimizationapplications and evaluation of an evolutionary algorithm /[electronic resource] :by Micael Couceiro, Pedram Ghamisi. - Cham :Springer International Publishing :2016. - x, 75 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in applied sciences and technology,2191-530X. - SpringerBriefs in applied sciences and technology..

Particle Swarm Optimization (PSO) -- Fractional Order Darwinian PSO (FODPSO) -- Case Study I: Curve Fitting -- Case Study II: Image Segmentation -- Case Study III: Swarm Robotics -- Conclusions.

This book examines the bottom-up applicability of swarm intelligence to solving multiple problems, such as curve fitting, image segmentation, and swarm robotics. It compares the capabilities of some of the better-known bio-inspired optimization approaches, especially Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO) and the recently proposed Fractional Order Darwinian Particle Swarm Optimization (FODPSO), and comprehensively discusses their advantages and disadvantages. Further, it demonstrates the superiority and key advantages of using the FODPSO algorithm, such as its ability to provide an improved convergence towards a solution, while avoiding sub-optimality. This book offers a valuable resource for researchers in the fields of robotics, sports science, pattern recognition and machine learning, as well as for students of electrical engineering and computer science.

ISBN: 9783319196350$q(electronic bk.)

Standard No.: 10.1007/978-3-319-19635-0doiSubjects--Topical Terms:

577800
Swarm intelligence.

LC Class. No.: Q337.3

Dewey Class. No.: 006.3824

Fractional order Darwinian particle swarm optimization = applications and evaluation of an evolutionary algorithm /
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245 1 0 $a Fractional order Darwinian particle swarm optimization $h [electronic resource] : $b applications and evaluation of an evolutionary algorithm / $c by Micael Couceiro, Pedram Ghamisi.
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300 $a x, 75 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in applied sciences and technology, $x 2191-530X
505 0 $a Particle Swarm Optimization (PSO) -- Fractional Order Darwinian PSO (FODPSO) -- Case Study I: Curve Fitting -- Case Study II: Image Segmentation -- Case Study III: Swarm Robotics -- Conclusions.
520 $a This book examines the bottom-up applicability of swarm intelligence to solving multiple problems, such as curve fitting, image segmentation, and swarm robotics. It compares the capabilities of some of the better-known bio-inspired optimization approaches, especially Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO) and the recently proposed Fractional Order Darwinian Particle Swarm Optimization (FODPSO), and comprehensively discusses their advantages and disadvantages. Further, it demonstrates the superiority and key advantages of using the FODPSO algorithm, such as its ability to provide an improved convergence towards a solution, while avoiding sub-optimality. This book offers a valuable resource for researchers in the fields of robotics, sports science, pattern recognition and machine learning, as well as for students of electrical engineering and computer science.
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