'Particle swarm' diaporamas de présentation

COMP 4106

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The Particle Swarm: Theme and Variations on Computational Social Learning

The Particle Swarm: Theme and Variations on Computational Social Learning. James Kennedy Washington, DC Kennedy.Jim@gmail.com. The Particle Swarm. A stochastic, population-based algorithm for problem-solving Based on a social-psychological metaphor

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Modern Heuristic Optimization Techniques and Potential Applications to Power System Control

Modern Heuristic Optimization Techniques and Potential Applications to Power System Control. Mohamed A El-Sharkawi The CIA lab Department of Electrical Engineering University of Washington Seattle, WA 98195-2500 elsharkawi@ee.washington.edu http://cialab.ee.washington.edu.

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Windings For Permanent Magnet Machines Yao Duan, R. G. Harley and T. G. Habetler

Windings For Permanent Magnet Machines Yao Duan, R. G. Harley and T. G. Habetler Georgia Institute of Technology. OUTLINE. Introduction Overall Design Procedure Analytical Design Model Optimization Comparison Conclusions. Introduction.

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Cellular Neural Networks Training and Implementation on a GPU Cluster

Cellular Neural Networks Training and Implementation on a GPU Cluster. Bipul Luitel , Cameron Johnson & Sinchan Roychowdhury Real-Time Power and Intelligent Systems Lab, Missouri S&T, Rolla, MO. Outline .

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Firefly Algorithm By Rasool Tavakoli

Firefly Algorithm By Rasool Tavakoli. Outline. Abstract Introduction Particle Swarm Optimization Firefly Algorithm Comparison of FA with PSO and GA Conclusions References. Abstract. Nature-inspired algorithms are among the most powerful algorithms for optimization.

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NASA/MSFC and UAH Viva 2.4.1/3.0 Starbridge Hypercomputer (HC-36) Clint Patrick/EV23

Overview. Application. Neural Network Implementation Optimization Genetic Algorithm Particle Swarm. NASA/MSFC and UAH Viva 2.4.1/3.0 Starbridge Hypercomputer (HC-36) Clint Patrick/EV23. Implementation Results. Tool Pluses and Minuses. Optimization

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Soft Computing Applications

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Particle Swarm Optimization (PSO)

Particle Swarm Optimization (PSO). MATH 3220 Student Presentations By: Jennifer Lamb. Outline. Definition History How it works Scenario/Key words Pseudo-Code Algorithm Application Advantages/Disadvantages Works Cited. What is PSO?. How did it get its name?

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Professor: Ming- Shyan Wang Student: Yi-Ting Lin

Missing-Sensor-Fault-Tolerant Control for SSSC FACTS Device With Real-Time Implementation Wei Qiao , Member, IEEE, Ganesh Kumar Venayagamoorthy , Senior Member, IEEE, and Ronald G. Harley, Fellow, IEEE IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 24, NO. 2, APRIL 2009.

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COMPE 564/ MODES 662 Natural Computing 201 3 Fall

COMPE 564/ MODES 662 Natural Computing 201 3 Fall Murat KARAKAYA Department of Computer Engineering. COMPE 564 / MODES 662 Natural Computing. Instructor s : Murat KARAKAYA Email : kmkarakaya@ atilim.edu.tr Office : Z-14 Lecture : Wednesday 14:30-17:20 @ 2031

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Dr.-Ing. Sanaz Mostaghim

Dr.-Ing. Sanaz Mostaghim. Major Research Interests: Nature Inspired Optimization Methods Evolutionary Algorithms Swarm Intelligence (Particle Swarm Optimization) Multi-objective Optimization Organic Computing Grid Computing Summer Semester 2006:

624 views • 1 slides