Particle Swarm Optimization: A Physics-based Approach

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Morgan & Claypool Publishers, 2008 - Science - 93 pages
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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. Table of Contents: Introduction / The Classical Particle Swarm Optimization Method / Boundary Conditions for the PSO Method / The Quantum Particle Swarm Optimization / Bibliography /Inde
 

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Contents

Introduction
1
12 Why PhysicsBased Approach
4
13 The Philosophy of the Book
5
The Classical Particle Swarm Optimization Method
7
22 Particle Swarm Optimization and ElectroMagnetics
10
Physical Formalism for Particle Swarm Optimization
13
33 Extraction of Information from Swarm Dynamics
20
34 Thermodynamic Analysis of the PSO Environment
21
43 The Hard Boundary Conditions
44
45 Hybrid Periodic Boundary Condition for the PSO Environment
46
The Quantum Particle Swarm Optimization
57
52 The Choice of the Potential Well Distribution
59
53 The Collapse of the Wave Function
60
54 Selecting the Parameters of the Algorithm
61
55 The QPSO Algorithm
62
56 Application of the QPSO Algorithm to Array Antenna Synthesis Problems
63

35 Acceleration Technique for the PSO Algorithm
30
36 Diffusion Model for the PSO Algorithm
32
37 Markov Model for Swarm Optimization Techniques
35
Boundary Conditions for the PSO Method
41
42 The Soft Conditions
43
57 Infinitesimal Dipoles Equivalent to Practical Antennas
69
58 Conclusion
76
Bibliography
79
Index
85
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University of Mississippi

University of Mississippi

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