Particle swarm optimization : a physics-based approach /
By: Mikki, Said M.
Contributor(s): Kishk, Ahmed A.
Material type:
BookSeries: Synthesis lectures on computational electromagnetics: #20.Publisher: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, c2008Description: 1 electronic text (x, 93 p. : ill. (some col.)) : digital file.ISBN: 1598296159 (electronic bk.); 9781598296150 (electronic bk.); 1598296140 (pbk.); 9781598296143 (pbk.).Uniform titles: Synthesis digital library of engineering and computer science. Subject(s): Mathematical optimization | Particle swarm optimization | Swarm dynamics | Computational electromagnetics | Evolutionary computing | Artificial intelligence | Optimization algorithmDDC classification: 539.7/2 Online resources: Abstract with links to resource Also available in print.| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
E books
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PK Kelkar Library, IIT Kanpur | Available | EBKE111 |
Mode of access: World Wide Web.
System requirements: Adobe Acrobat reader.
Part of: Synthesis digital library of engineering and computer science.
Series from website.
Includes bibliographical references (p. 79-84) and index.
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.
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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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.
Also available in print.
Title from PDF t.p. (viewed Oct. 19, 2008).
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