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
| 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.
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.
