Fractional Order Darwinian Particle Swarm Optimization

Applications and Evaluation of an Evolutionary Algorithm

Nonfiction, Science & Nature, Science, Other Sciences, System Theory, Computers, Advanced Computing, Artificial Intelligence, General Computing
Cover of the book Fractional Order Darwinian Particle Swarm Optimization by Micael Couceiro, Pedram Ghamisi, Springer International Publishing
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Author: Micael Couceiro, Pedram Ghamisi ISBN: 9783319196350
Publisher: Springer International Publishing Publication: June 16, 2015
Imprint: Springer Language: English
Author: Micael Couceiro, Pedram Ghamisi
ISBN: 9783319196350
Publisher: Springer International Publishing
Publication: June 16, 2015
Imprint: Springer
Language: English

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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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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