Fractional Order Darwinian Particle Swarm Optimization Applications and Evaluation of an Evolutionary Algorithm /

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Couceiro, Micael (Autor), Ghamisi, Pedram (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edición:1st ed. 2016.
Colección:SpringerBriefs in Applied Sciences and Technology,
Temas:
Computational intelligence.
Artificial intelligence.
System theory.
Control theory.
Computational Intelligence.
Artificial Intelligence.
Systems Theory, Control .
Acceso en línea:Texto Completo

MARC

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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. 27 illus., 24 illus. in color.  |b online resource. 
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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. 
650 0 |a Computational intelligence. 
650 0 |a Artificial intelligence. 
650 0 |a System theory. 
650 0 |a Control theory. 
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650 2 4 |a Systems Theory, Control . 
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950 |a Engineering (R0) (SpringerNature-43712) 

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