|
|
|
|
LEADER |
00000cam a2200000 i 4500 |
001 |
EBSCO_ocn852898523 |
003 |
OCoLC |
005 |
20231017213018.0 |
006 |
m o d |
007 |
cr ||||||||||| |
008 |
130321s2013 nyu ob 001 0 eng |
010 |
|
|
|a 2020676520
|
040 |
|
|
|a DLC
|b eng
|e rda
|c DLC
|d YDXCP
|d E7B
|d OCLCF
|d EBLCP
|d DEBSZ
|d AGLDB
|d VTS
|d AU@
|d STF
|d M8D
|d N$T
|d OCLCO
|d OCLCQ
|d OCLCO
|
020 |
|
|
|a 9781624177972
|q (epub)
|
020 |
|
|
|a 1624177972
|
020 |
|
|
|z 9781624177965
|q (hardcover)
|
020 |
|
|
|z 1624177964
|
029 |
1 |
|
|a AU@
|b 000062326789
|
029 |
1 |
|
|a DEBSZ
|b 449550192
|
029 |
1 |
|
|a DEBSZ
|b 429935048
|
029 |
1 |
|
|a DEBBG
|b BV043091292
|
035 |
|
|
|a (OCoLC)852898523
|
042 |
|
|
|a pcc
|
050 |
0 |
0 |
|a QA402.5
|
072 |
|
7 |
|a MAT
|x 042000
|2 bisacsh
|
082 |
0 |
0 |
|a 519.6
|2 23
|
049 |
|
|
|a UAMI
|
245 |
0 |
0 |
|a Global optimization :
|b theory, developments and applications /
|c Angelika Michalski, editor.
|
264 |
|
1 |
|a New York :
|b Nova Publishers,
|c [2013]
|
300 |
|
|
|a 1 online resource.
|
336 |
|
|
|a text
|b txt
|2 rdacontent
|
337 |
|
|
|a computer
|b c
|2 rdamedia
|
338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
490 |
0 |
|
|a Mathematics Research Developments
|
490 |
0 |
|
|a Computational mathematics and analysis series
|
504 |
|
|
|a Includes bibliographical references and index.
|
588 |
|
|
|a Description based on print version record and CIP data provided by publisher.
|
505 |
0 |
|
|a GLOBAL OPTIMIZATION ; Library of Congress Cataloging-in-Publication Data; CONTENTS; PREFACE ; Chapter 1 PARTICLE SWARM OPTIMIZATION WITH RE-INITIALIZATION STRATEGIES FOR CONTINUOUS GLOBAL OPTIMIZATION ; ABSTRACT ; NOMENCLATURE ; 1. INTRODUCTION ; 2. DESCRIPTION AND DEVELOPMENT OF PSO ; 3. PARTICLE SWARM OPTIMIZATION WITH RE-INITIALIZATION STRATEGIES ; 3. 1. PSO with Selection Strategy ; 3. 2. PSO with Escape Strategy ; 3. 3. PSO with Both Selection and Escape Strategies ; 4. RESULTS AND DISCUSSION; 4. 1. Description of Benchmark Problems ; 4. 2. Performance of Proposed PSO.
|
505 |
8 |
|
|a 4.3. Comparison of PPSO with Recent PSO Algorithms CONCLUSION ; APPENDIX A: ORTHOGONAL MATRIX ; REFERENCES ; Chapter 2 PARTICLE SWARM GLOBAL OPTIMIZATION OF ORBITAL MANEUVERS ; ABSTRACT ; 1. INTRODUCTION ; 2. DESCRIPTION OF THE METHOD ; 2.1. Unconstrained Optimization; 2.2. Constrained Optimization ; 3. OPTIMAL IMPULSIVE TRANSFERS BETWEEN COPLANAR CIRCULAR ORBITS ; 3.1. Problem Definition ; 3.2. Numerical Results ; 4. OPTIMAL TWO-IMPULSE TRANSFERS BETWEEN NON-COPLANAR ORBITS ; 4.1. Problem Definition ; 4.2. Numerical Results ; 5. OPTIMAL FOUR-IMPULSE ORBITAL RENDEZVOUS.
|
505 |
8 |
|
|a 5.1. Problem Definition 5.2. Numerical Results ; 6. OPTIMAL LOW EARTH ORBIT -- LOW MOON ORBIT TRANSFER ; 6.1. Problem Definition ; 6.2. Numerical Results ; CONCLUSION ; REFERENCES ; Chapter 3 FLOAT-ENCODED GENETIC ALGORITHM USED FOR THE INVERSION PROCESSING OF WELL-LOGGING DATA ; ABSTRACT ; 1. INTRODUCTION ; 2. THE INVERSION METHODOLOGY ; 2.1. Forward Modeling ; 2.2. The Inverse Problem ; 2.2.1. Local Inversion Methods ; 2.2.2. Interval Inversion Method ; 3. GENETIC ALGORITHM OPTIMIZATION ; 4. INVERSION EXPERIMENT USING SYNTHETIC DATA ; 5. FIELD CASES.
|
505 |
8 |
|
|a 5.1. Application to Water Prospecting 5.2. Application to Hydrocarbon Exploration ; CONCLUSION ; ACKNOWLEDGMENTS ; REFERENCES ; Chapter 4 THE PARTICLE COLLISION ALGORITHM: A METROPOLIS OPTIMIZATION METHOD ; ABSTRACT ; 1. INTRODUCTION ; 2. THE CANONICAL PCA ; Initialization Step ; Main Step ; Exploitation ; Scattering ; 3. APPLICATIONS OF THE PCA IN THE LITERAURE ; 3.1. Nuclear Engineering Problems ; 3.2. Inverse Problems ; 3.3. Course-Timetabling Problems; 4. VARIANTS OF THE PCA; 4.1. Other Local Searching Schemes ; Initialization Step ; Main Step.
|
505 |
8 |
|
|a Deterministic Local Search Algorithm (DLSA) Scattering ; 4.2. Multiple-Particle Variants; Initialization Step ; Main Step ; Exploitation ; Scattering ; New Population; Initialization Step ; Main Step ; Exploitation; Scattering ; 4.3. Multi-Neighborhood PCA ; 5. HYBRID APPROACHES; CONCLUSION; ACKNOWLEDGMENTS ; REFERENCES ; Chapter 5 CLASSIFIER-ASSISTED FRAMEWORKS FOR COMPUTATIONALLY EXPENSIVE OPTIMIZATION PROBLEMS; Abstract; 1. Introduction; 2. Background; 2.1. Expensive optimization problems and computational intelligence algorithms; 2.2. Simulator-infeasible vectors; 3. A baseline framework.
|
590 |
|
|
|a eBooks on EBSCOhost
|b EBSCO eBook Subscription Academic Collection - Worldwide
|
650 |
|
0 |
|a Mathematical optimization.
|
650 |
|
6 |
|a Optimisation mathématique.
|
650 |
|
7 |
|a MATHEMATICS
|x Optimization.
|2 bisacsh
|
650 |
|
7 |
|a Mathematical optimization
|2 fast
|
700 |
1 |
|
|a Michalski, Angelika,
|e editor.
|
776 |
0 |
8 |
|i Print version:
|t Global optimization
|d New York : Nova Publishers, [2013]
|z 9781624177965 (hardcover)
|w (DLC) 2012050351
|
856 |
4 |
0 |
|u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=608844
|z Texto completo
|
938 |
|
|
|a YBP Library Services
|b YANK
|n 10862161
|
938 |
|
|
|a EBSCOhost
|b EBSC
|n 608844
|
938 |
|
|
|a ebrary
|b EBRY
|n ebr10733707
|
938 |
|
|
|a ProQuest Ebook Central
|b EBLB
|n EBL3022685
|
994 |
|
|
|a 92
|b IZTAP
|