000			04785nam a2200589 i 4500
001			6812504
003			IEEE
005			20200413152850.0
006			m eo d
007			cr cn |||m|||a
008			081019s2008 caua fob 001 0 eng d
020			_a1598296159 (electronic bk.)
020			_a9781598296150 (electronic bk.)
020			_a1598296140 (pbk.)
020			_a9781598296143 (pbk.)
024	7		_a10.2200/S00110ED1V01Y200804CEM020 _2doi
035			_a(OCoLC)231628508
035			_a(CaBNVSL)gtp00531432
040			_aCaBNVSL _cCaBNVSL _dCaBNVSL
050		4	_aQC20.7.M27 _b.M546 2008
082	0	4	_a539.7/2 _222
100	1		_aMikki, Said M.
245	1	0	_aParticle swarm optimization _h[electronic resource] : _ba physics-based approach / _cSaid M. Mikki and Ahmed A. Kishk.
260			_aSan Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool Publishers, _cc2008.
300			_a1 electronic text (x, 93 p. : ill. (some col.)) : _bdigital file.
490	1		_aSynthesis lectures on computational electromagnetics, _x1932-1716 ; _v#20
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat reader.
500			_aPart of: Synthesis digital library of engineering and computer science.
500			_aSeries from website.
504			_aIncludes bibliographical references (p. 79-84) and index.
505	0		_aContents -- 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.
506	1		_aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510	0		_aCompendex
510	0		_aINSPEC
510	0		_aGoogle scholar
510	0		_aGoogle book search
520			_aThis 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.
530			_aAlso available in print.
588			_aTitle from PDF t.p. (viewed Oct. 19, 2008).
650		0	_aMathematical optimization.
690			_aParticle swarm optimization.
690			_aSwarm dynamics.
690			_aComputational electromagnetics.
690			_aEvolutionary computing.
690			_aArtificial intelligence.
690			_aOptimization algorithm.
700	1		_aKishk, Ahmed A.
730	0		_aSynthesis digital library of engineering and computer science.
830		0	_aSynthesis lectures on computational electromagnetics, _x1932-1716 ; _v#20.
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6812504
999			_c561611 _d561611