Particle swarm optimization a physics-based approach /

Particle swarm optimization [electronic resource] : a physics-based approach / Said M. Mikki and Ahmed A. Kishk.

This work aims to provide new introduction to the particle swarm optimization methods using a formal analogy with physical systems. By postulating that the swarm motion behaves similar to both classical and quantum particles, we establish a direct connection between what are usually assumed to be se...

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Bibliographic Details
Online Access: Full Text (via Morgan & Claypool)
Main Author: Mikki, Said M.
Other Authors: Kishk, Ahmed A.
Other title:Synthesis digital library of engineering and computer science.
Format: Electronic eBook
Language:English
Published: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, ©2008.
Series:Synthesis lectures on computational electromagnetics (Online) ; # 20.
Subjects:
Mathematical optimization.

MARC

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245 1 0 |a Particle swarm optimization  |h [electronic resource] :  |b a physics-based approach /  |c Said M. Mikki and Ahmed A. Kishk. 
260 |a San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) :  |b Morgan & Claypool Publishers,  |c ©2008. 
300 |a 1 electronic text (x, 93 pages : illustrations (some color)) :  |b digital file. 
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337 |a computer  |b c  |2 rdamedia. 
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490 1 |a Synthesis lectures on computational electromagnetics,  |x 1932-1716 ;  |v #20. 
500 |a Part of: Synthesis digital library of engineering and computer science. 
500 |a Title from PDF title page (viewed Oct. 19, 2008) 
500 |a Series from website. 
504 |a Includes bibliographical references (pages 79-84) and index. 
505 0 |a Contents -- Preface -- 1. Introduction -- 1.1. What is optimization? -- 1.2. Why physics-based approach -- 1.3. The philosophy of the book -- 2. The classical particle swarm optimization method -- 2.1. Definition of the PSO algorithm -- 2.2. Particle swarm optimization and electromagnetics -- 3. Physical formalism for particle swarm optimization -- 3.1. Introduction -- 3.2. Molecular dynamics formulation -- 3.3. Extraction of information from swarm dynamics -- 3.4. Thermodynamic analysis of the PSO environment -- 3.5. Acceleration technique for the PSO algorithm -- 3.6. Diffusion model for the PSO algorithm -- 3.7. Markov model for swarm optimization techniques -- 4. Boundary conditions for the PSO method -- 4.1. Introduction -- 4.2. The soft conditions -- 4.3. The hard boundary conditions -- 4.4. Comparative study of hard and soft boundary conditions -- 4.5. Hybrid periodic boundary condition for the PSO environment -- 5. The quantum particle swarm optimization -- 5.1. Quantum formulation of the swarm dynamics -- 5.2. The choice of the potential well distribution -- 5.3. The collapse of the wave function -- 5.4. Selecting the parameters of the algorithm -- 5.5. The QPSO algorithm -- 5.6. Application of the QPSO to array antenna synthesis problems -- 5.7. Infinitesimal dipoles equivalent to practical antennas -- 5.8. Conclusion -- Bibliography -- Index. 
520 |a This work aims to provide new introduction to the particle swarm optimization methods using a formal analogy with physical systems. By postulating that the swarm motion behaves similar to both classical and quantum particles, we establish a direct connection between what are usually assumed to be separate fields of study, optimization and physics. Within this framework, it becomes quite natural to derive the recently introduced quantum PSO algorithm from the Hamiltonian or the Lagrangian of the dynamical system. The physical theory of the PSO is used to suggest some improvements in the algorithm itself, like temperature acceleration techniques and the periodic boundary condition. At the end, we provide a panorama of applications demonstrating the power of the PSO, classical and quantum, in handling difficult engineering problems. The goal of this work is to provide a general multi-disciplinary view on various topics in physics, mathematics, and engineering by illustrating their interdependence within the unified framework of the swarm dynamics. 
650 0 |a Mathematical optimization.  |0 http://id.loc.gov/authorities/subjects/sh85082127. 
690 |a Particle swarm optimization. 
690 |a Swarm dynamics. 
690 |a Computational electromagnetics. 
690 |a Evolutionary computing. 
690 |a Artificial intelligence. 
690 |a Optimization algorithm. 
700 1 |a Kishk, Ahmed A.  |0 http://id.loc.gov/authorities/names/no2008085832. 
730 0 |a Synthesis digital library of engineering and computer science.  |0 http://id.loc.gov/authorities/names/n2016188085. 
830 0 |a Synthesis lectures on computational electromagnetics (Online) ;  |v # 20.  |0 http://id.loc.gov/authorities/names/no2006002041. 
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