Computational optimization, methods and algorithms:
Gespeichert in:
| Weitere beteiligte Personen: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2011
|
| Schriftenreihe: | Studies in computational intelligence
356 |
| Schlagwörter: | |
| Links: | http://deposit.dnb.de/cgi-bin/dokserv?id=3708398&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024178023&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Literaturangaben |
| Umfang: | XV, 281 S. graph. Darst. |
| ISBN: | 9783642208584 3642208584 |
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Datensatz im Suchindex
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| adam_text | IMAGE 1
TABLE OF CONTENTS
COMPUTATIONAL OPTIMIZATION: AN OVERVIEW 1
XIN-SHE YANG, SLAWOMIR KOZIEL 1.1 INTRODUCTION 1
1.2 COMPUTATIONAL OPTIMIZATION 2
1.3 OPTIMIZATION PROCEDURE 3
1.4 OPTIMIZER 4
1.4.1 OPTIMIZATION ALGORITHMS 4
1.4.2 CHOICE OF ALGORITHMS 7
1.5 SIMULATOR 8
1.5.1 NUMERICAL SOLVERS 8
1.5.2 SIMULATION EFFICIENCY 9
1.6 LATEST DEVELOPMENTS 10
REFERENCES 10
OPTIMIZATION ALGORITHMS 13
XIN-SHE YANG 2.1 INTRODUCTION 13
2.2 DERIVATIVE-BASED ALGORITHMS 14
2.2.1 NEWTON S METHOD AND HILL-CLIMBING 14
2.2.2 CONJUGATE GRADIENT METHOD 15
2.3 DERIVATIVE-FREE ALGORITHMS 16
2.3.1 PATTERN SEARCH 16
2.3.2 TRUST-REGION METHOD 17
2.4 METAHEURISTIC ALGORITHMS 18
2.4.1 SIMULATED ANNEALLING 18
2.4.2 GENETIC ALGORITHMS AND DIFFERENTIAL EVOLUTION 19 2.4.3 PARTICLE
SWARM OPTIMIZATION 21
2.4.4 HARMONY SEARCH 22
2.4.5 FIREFLY ALGORITHM 23
2.4.6 CUCKOO SEARCH 24
2.5 A UNIFIED APPROACH TO METAHEURISTICS 26
2.5.1 CHARACTERISTICS OF METAHEURISTICS 26
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1010804006
DIGITALISIERT DURCH
IMAGE 2
XII TABLE OF CONTENTS
2.6 GENERALIZED EVOLUTIONARY WALK ALGORITHM (GEWA) 27 2.6.1 TO BE
INSPIRED OR NOT TO BE INSPIRED 29
REFERENCES 29
3 SURROGATE-BASED METHODS 33
SLAWOMIR KOZIEL, DAVID ECHEVERRIA CIAURRI, LEIFUR LEIFSSON 3.1
INTRODUCTION 34
3.2 SURROGATE-BASED OPTIMIZATION 35
3.3 SURROGATE MODELS 37
3.3.1 DESIGN OF EXPERIMENTS 39
3.3.2 SURROGATE MODELING TECHNIQUES 41
3.3.3 MODEL VALIDATION 45
3.3.4 SURROGATE CORRECTION 45
3.4 SURROGATE-BASED OPTIMIZATION TECHNIQUES 49
3.4.1 APPROXIMATION MODEL MANAGEMENT OPTIMIZATION 50 3.4.2 SPACE MAPPING
50
3.4.3 MANIFOLD MAPPING 52
3.4.4 SURROGATE MANAGEMENT FRAMEWORK 53
3.4.5 EXPLOITATION VERSUS EXPLORATION 55
3.5 FINAL REMARKS 55
REFERENCES 56
4 DERIVATIVE-FREE OPTIMIZATION 61
OLIVER KRAMER, DAVID ECHEVERRIA CIAURRI, SLAWOMIR KOZIEL 4.1
INTRODUCTION 61
4.2 DERIVATIVE-FREE OPTIMIZATION 63
4.3 LOCAL OPTIMIZATION 65
4.3.1 PATTERN SEARCH METHODS 65
4.3.2 DERIVATIVE-FREE OPTIMIZATION WITH INTERPOLATION AND APPROXIMATION
MODELS 67
4.4 GLOBAL OPTIMIZATION 68
4.4.1 EVOLUTIONARY ALGORITHMS 68
4.4.2 ESTIMATION OF DISTRIBUTION ALGORITHMS 72
4.4.3 PARTICLE SWARM OPTIMIZATION 73
4.4.4 DIFFERENTIAL EVOLUTION 73
4.5 GUIDELINES FOR GENERALLY CONSTRAINED OPTIMIZATION 74 4.5.1 PENALTY
FUNCTIONS 74
4.5.2 AUGMENTED LAGRANGIAN METHOD 75
4.5.3 FILTER METHOD 76
4.5.4 OTHER APPROACHES 77
4.6 CONCLUDING REMARKS 78
REFERENCES 79
IMAGE 3
TABLE OF CONTENTS XIII
5 MAXIMUM SIMULATED LIKELIHOOD ESTIMATION: TECHNIQUES AND APPLICATIONS
IN ECONOMICS 85
IVAN JELIAZKOV, ALICIA LLORO 5.1 INTRODUCTION 85
5.2 COPULA MODEL 86
5.3 ESTIMATION METHODOLOGY 90
5.3.1 THE CRT METHOD 90
5.3.2 OPTIMIZATION TECHNIQUE 91
5.4 APPLICATION 94
5.5 CONCLUDING REMARKS 98
REFERENCES 99
6 OPTIMIZING COMPLEX MULTI-LOCATION INVENTORY MODELS USING PARTICLE
SWARM OPTIMIZATION 101
CHRISTIAN A. HOCHMUTH, JOERG LAESSIG, STEFANIE THIEM 6.1 INTRODUCTION 101
6.2 RELATED WORK 102
6.3 SIMULATION OPTIMIZATION 103
6.4 MULTI-LOCATION INVENTORY MODELS WITH LATERAL TRANSSHIPMENTS . .. 105
6.4.1 FEATURES OF A GENERAL MODEL 105
6.4.2 FEATURES OF THE SIMULATION MODEL 107
6.5 PARTICLE SWARM OPTIMIZATION ILL
6.6 EXPERIMENTATION 112
6.6.1 SYSTEM SETUP 112
6.6.2 RESULTS AND DISCUSSION 115
6.7 CONCLUSION AND FUTURE WORK 121
REFERENCES 122
7 TRADITIONAL AND HYBRID DERIVATIVE-FREE OPTIMIZATION APPROACHES FOR
BLACK BOX FUNCTIONS 125
GENETHA ANNE GRAY, KATHLEEN R. FOWLER 7.1 INTRODUCTION AND MOTIVATION
126
7.2 A MOTIVATING EXAMPLE 127
7.3 SOME TRADITIONAL DERIVATIVE-FREE OPTIMIZATION METHODS 130 7.3.1
GENETIC ALGORITHMS (GAS) 130
7.3.2 DETERMINISTIC SAMPLING METHODS 132
7.3.3 STATISTICAL EMULATION 138
7.4 SOME DFO HYBRIDS 139
7.4.1 APPS-TGP 140
7.4.2 EAGLS 142
7.4.3 DIRECT-IFFCO 144
7.4.4 DIRECT-TGP 144
7.5 SUMMARY AND CONCLUSION 145
REFERENCES 146
IMAGE 4
XIV TABLE OF CONTENTS
8 SIMULATION-DRIVEN DESIGN IN MICROWAVE ENGINEERING: METHODS 153
SLAWOMIR KOZIEL, STANISLAV OGURTSOV 8.1 INTRODUCTION 153
8.2 DIRECT APPROACHES 154
8.3 SURROGATE-BASED DESIGN OPTIMIZATION 156
8.4 SURROGATE MODELS FOR MICROWAVE ENGINEERING 158
8.5 MICROWAVE SIMULATION-DRIVEN DESIGN EXPLOITING PHYSICALLY-BASED
SURROGATES 161
8.5.1 SPACE MAPPING 162
8.5.2 SIMULATION-BASED TUNING AND TUNING SPACE MAPPING 163 8.5.3
SHAPE-PRESERVING RESPONSE PREDICTION 167
8.5.4 MULTI-FIDELITY OPTIMIZATION USING COARSE-DISCRETIZATION EM MODELS
170
8.5.5 OPTIMIZATION USING ADAPTIVELY ADJUSTED DESIGN SPECIFICATIONS 172
8.6 SUMMARY 175
REFERENCES 175
9 VARIABLE-FIDELITY AERODYNAMIC SHAPE OPTIMIZATION 179 LEIFUR LEIFSSON,
SLAWOMIR KOZIEL 9.1 INTRODUCTION 179
9.2 PROBLEM FORMULATION 182
9.3 COMPUTATIONAL FLUID DYNAMIC MODELING 185
9.3.1 GOVERNING EQUATIONS 185
9.3.2 NUMERICAL MODELING 188
9.4 DIRECT OPTIMIZATION 195
9.4.1 GRADIENT-BASED METHODS 196
9.4.2 DERIVATIVE-FREE METHODS 197
9.5 SURROGATE-BASED OPTIMIZATION 197
9.5.1 THE CONCEPT 197
9.5.2 SURROGATE MODELING 198
9.5.3 OPTIMIZATION TECHNIQUES 200
9.6 SUMMARY 204
REFERENCES 205
10 EVOLUTIONARY ALGORITHMS APPLIED TO MULTI-OBJECTIVE AERODYNAMIC SHAPE
OPTIMIZATION 211
ALFREDO ARIAS-MONTANO, CARLOS A. COELLO COELLO, EFREN MEZURA-MONTES 10.1
INTRODUCTION 212
10.2 BASIC CONCEPTS 213
10.2.1 PARETO DOMINANCE 214
10.2.2 PARETO OPTIMALITY 214
10.2.3 PARETO FRONT 215
10.3 MULTI-OBJECTIVE AERODYNAMIC SHAPE OPTIMIZATION 215 10.3.1 PROBLEM
DEFINITION 215
IMAGE 5
TABLE OF CONTENTS XV
10.3.2 SURROGATE-BASED OPTIMIZATION 216
10.3.3 HYBRID MOEA OPTIMIZATION 219
10.3.4 ROBUST DESIGN OPTIMIZATION 220
10.3.5 MULTI-DISCIPLINARY DESIGN OPTIMIZATION 222
10.3.6 DATA MINING AND KNOWLEDGE EXTRACTION 224
10.4 A CASE STUDY 226
10.4.1 OBJECTIVE FUNCTIONS 226
10.4.2 GEOMETRY PARAMETERIZATION 226
10.4.3 CONSTRAINTS 229
10.4.4 EVOLUTIONARY ALGORITHM 229
10.4.5 RESULTS 233
10.5 CONCLUSIONS AND FINAL REMARKS 236
REFERENCES 237
11 AN ENHANCED SUPPORT VECTOR MACHINES MODEL FOR CLASSIFICATION AND RULE
GENERATION 24 1
PING-FENG PAI, MING-FU HSU 11.1 BASIC CONCEPT OF CLASSIFICATION AND
SUPPORT VECTOR MACHINES 241 11.2 DATA PREPROCESSING 245
11.2.1 DATA CLEANING 245
11.2.2 DATA TRANSFORMATION 245
11.2.3 DATA REDUCTION 246
11.3 PARAMETER DETERMINATION OF SUPPORT VECTOR MACHINES BY
META-HEURISTICS 247
11.3.1 GENETIC ALGORITHM 247
11.3.2 IMMUNE ALGORITHM 248
11.3.3 PARTICLE SWARM OPTIMIZATION 249
11.4 RULE EXTRACTION FORM SUPPORT VECTOR MACHINES 250
11.5 THE PROPOSED ENHANCED SVM MODEL 252
11.6 A NUMERICAL EXAMPLE AND EMPIRICAL RESULTS 253
11.7 CONCLUSION 255
REFERENCES 256
12 BENCHMARK PROBLEMS IN STRUCTURAL OPTIMIZATION 259
AMIR HOSSEIN GANDOMI, XIN-SHE YANG 12.1 INTRODUCTION TO BENCHMARK
STRUCTURAL DESIGN 259
12.1.1 STRUCTURAL ENGINEERING DESIGN AND OPTIMIZATION 260 12.2
CLASSIFICATIONS OF BENCHMARKS 261
12.3 DESIGN BENCHMARKS 262
12.3.1 TRUSS DESIGN PROBLEMS 262
12.3.2 NON-TRUSS DESIGN PROBLEMS 268
12.4 DISCUSSIONS AND FURTHER RESEARCH 278
REFERENCES 279
AUTHOR INDEX 283
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| title | Computational optimization, methods and algorithms |
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