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Fractional order Darwinian particle swarm optimization[electronic resource] :applications and evaluation of an evolutionary algorithm /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.3824
書名/作者:	Fractional order Darwinian particle swarm optimization : applications and evaluation of an evolutionary algorithm // by Micael Couceiro, Pedram Ghamisi.
作者:	Couceiro, Micael.
其他作者:	Ghamisi, Pedram.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	x, 75 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Swarm intelligence.
標題:	Mathematical optimization.
標題:	Evolution equations.
標題:	Engineering.
標題:	Computational Intelligence.
標題:	Artificial Intelligence (incl. Robotics)
標題:	Systems Theory, Control.
ISBN:	9783319196350
ISBN:	9783319196343
內容註:	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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-19635-0

Fractional order Darwinian particle swarm optimization[electronic resource] :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

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

470574
Swarm intelligence.

LC Class. No.: Q337.3

Dewey Class. No.: 006.3824

Fractional order Darwinian particle swarm optimization[electronic resource] :applications and evaluation of an evolutionary algorithm /
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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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