Metaheuristics for dynamic optimization:
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Berlin [u.a.]
Springer
2013
|
| Series: | Studies in computational intelligenc
433 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXXI, 398 S. Ill., graph. Darst. |
| ISBN: | 9783642306648 |
Staff View
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Record in the Search Index
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| adam_text | IMAGE 1
CONTENTS
1 PERFORMANCE ANALYSIS O F DYNAMIC OPTIMIZATION ALGORITHMS 1
AMIR NAKIB AND PATRICK SIARRY 1.1 INTRODUCTION 1
1.2 PERFORMANCE ANALYSIS TOOLS O F OPTIMIZATION ALGORITHMS 3
1.3 THE MOVING PEAKS BENCHMARK . 8
1.4 THE GENERALIZED DYNAMIC BENCHMARK GENERATOR 10
1.5 DYNAMIC MULTIOBJECTIVE OPTIMIZATION BENCHMARK 13
1.6 CONCLUSIONS 14
REFERENCES 14
2 QUANTITATIVE PERFORMANCE MEASURES FOR DYNAMIC OPTIMIZATION PROBLEMS 17
BRISEIDA SARASOLA AND ENRIQUE ALBA 2.1 INTRODUCTION 17
2.2 BACKGROUND AND MOTIVATION 18
2.3 A MEASURE FOR DEGRADATION: ^DEGRADATION 21
2.4 THE MOVING PEAKS PROBLEM 22
2.5 EXPERIMENTAL SETTING 22
2.6 A GENERAL PERFORMANCE MEASURE FOR DOPS: ABC 27
2.7 CONCLUSIONS 32
REFERENCES 32
3 DYNAMIC FUNCTION OPTIMIZATION: THE MOVING PEAKS BENCHMARK 35
IRENE MOSER AND RAYMOND CHIONG 3.1 INTRODUCTION 35
3.2 BACKGROUND 36
3.3 EXISTING APPROACHES 38
3.4 COMPARISON AND DISCUSSION 52
3.5 CONCLUSIONS 55
REFERENCES 55
HTTP://D-NB.INFO/1021849138
IMAGE 2
XII
CONTENTS
4 SRCS: A TECHNIQUE FOR COMPARING MULTIPLE ALGORITHMS UNDER
SEVERAL FACTORS IN DYNAMIC OPTIMIZATION PROBLEMS 61
IGNACIO G. DEL AMO AND DAVID A. PELTA 4.1 INTRODUCTION 61
4.2 TYPICAL RESEARCH CASE: COMPARING MULTIPLE ALGORITHMS OVER SEVERAL
CONFIGURATIONS O F A PROBLEM 63
4.3 SRCS: STATISTICAL RANKING COLOR SCHEME 7 0
4.4 SOME CONSIDERATIONS ON STATISTICAL TESTS 73
4.5 CONCLUSIONS 7 4
REFERENCES 7 6
5 DYNAMIC COMBINATORIAL OPTIMIZATION PROBLEMS: A FITNESS LANDSCAPE
ANALYSIS 79
PHILIPP ROHLFSHAGEN AND XIN YAO 5.1 INTRODUCTION 7 9
5.2 EVOLUTIONARY DYNAMIC OPTIMIZATION 80
5.3 DYNAMIC FITNESS LANDSCAPES 82
5.4 DYNAMIC FITNESS LANDSCAPES IN PRACTICE 86
5.5 UNDERSTANDING COMBINATORIAL PROBLEM DYNAMICS 9 0
5.6 CONCLUSIONS 95
REFERENCES 9 6
6 TWO APPROACHES FOR SINGLE AND MULTI-OBJECTIVE DYNAMIC OPTIMIZATION 99
KALYANMOY DEB 6.1 INTRODUCTION 100
6.2 SOLVING DYNAMIC OPTIMIZATION PROBLEMS 101
6.3 APPROACH 1: OFF-LINE DEVELOPMENT O F AN OPTIMAL RULE BASE . . . 102
6.4 APPROACH 2: ON-LINE OPTIMIZATION WITH A MINIMAL TIME WINDOW 109
6.5 CONCLUSIONS 114
REFERENCES 115
7 SELF-ADAPTIVE DIFFERENTIAL EVOLUTION FOR DYNAMIC ENVIRONMENTS WITH
FLUCTUATING NUMBERS O F OPTIMA 117
MATHYS C. DU PLESSIS AND ANDRIES P. ENGELBRECHT 7.1 INTRODUCTION 117
7.2 DYNAMIC ENVIRONMENTS 118
7.3 RELATED WORK 120
7.4 DIFFERENTIAL EVOLUTION 122
7.5 DYNAMIC POPULATION DIFFERENTIAL EVOLUTION 126
7.6 SELF-ADAPTIVE DYNPOPDE 137
7.7 CONCLUSIONS 143
REFERENCES 143
IMAGE 3
CONTENTS XIII
8 DYNAMIC MULTI-OBJECTIVE OPTIMIZATION USING PSO 147
MARDE HELBIG AND ANDRIES P. ENGELBRECHT 8.1 INTRODUCTION 147
8.2 BACKGROUND 148
8.3 DYNAMIC VECTOR EVALUATED PARTICLE SWARM OPTIMISATION APPROACH 152
8.4 EXPERIMENTS 156
8.5 RESULTS 163
8.6 CONCLUSIONS 178
REFERENCES 187
9 ANT COLONY BASED ALGORITHMS FOR DYNAMIC OPTIMIZATION PROBLEMS 1 89
GUILLERMO LEGUIZAMON AND ENRIQUE ALBA 9.1 INTRODUCTION 189
9.2 ANT COLONY OPTIMIZATION 191
9.3 DYNAMIC OPTIMIZATION PROBLEMS (DOPS) 195
9.4 SOLVING DOPS WITH ACB ALGORITHMS 197
9.5 CONCLUSIONS 207
REFERENCES 207
10 ELASTIC REGISTRATION O F BRAIN CINE-MRI SEQUENCES USING MLSDO DYNAMIC
OPTIMIZATION ALGORITHM 211
JULIEN LEPAGNOT, AMIR NAKIB, HAMOUCHE OULHADJ, AND PATRICK SIARRY 10.1
INTRODUCTION 211
10.2 REGISTRATION PROCESS 214
10.3 THE MLSDO ALGORITHM 217
10.4 EXPERIMENTAL RESULTS AND DISCUSSION 219
10.5 CONCLUSIONS 224
REFERENCES 224
11 ARTIFICIAL IMMUNE SYSTEM FOR SOLVING DYNAMIC CONSTRAINED OPTIMIZATION
PROBLEMS 225
VICTORIA S. ARAGON, SUSANA C. ESQUIVEL, AND CARLOS A. COELLO 11.1
INTRODUCTION 226
11.2 PROBLEM STATEMENT 227
11.3 PREVIOUS RELATED WORK 227
11.4 OUR PROPOSED APPROACH 231
11.5 EXPERIMENTS 238
11.6 DISCUSSION O F RESULTS 243
11.7 CONCLUSIONS 258
REFERENCES 261
IMAGE 4
XIV
CONTENTS
12 METAHEURISTICS FOR DYNAMIC VEHICLE ROUTING 265
MOSTEPHA R. KHOUADJIA, BRISEIDA SARASOLA, ENRIQUE ALBA, EL-GHAZALI
TALBI, AND LAETITIA JOURDAN 12.1 INTRODUCTION 266
12.2 DYNAMIC VEHICLE ROUTING PROBLEM 266
12.3 SOLVING METHODS 273
12.4 DYNAMIC PERFORMANCE MEASURES 280
12.5 PERFORMANCE ASSESSMENT 282
12.6 CONCLUSIONS AND FUTURE WORK 284
REFERENCES 285
13 LOW-LEVEL HYBRIDIZATION O F SCATTER SEARCH AND PARTICLE FILTER FOR
DYNAMIC TSP SOLVING 291
JUAN JOSE PANTRIGO AND ABRAHAM DUARTE 13.1 INTRODUCTION 291
13.2 DYNAMIC TRAVELLING SALESMAN PROBLEM 293
13.3 SEQUENTIAL ESTIMATION ALGORITHM: PARTICLE FILTER 294
13.4 POPULATION BASED METAHEURISTIC: SCATTER SEARCH 295
13.5 SCATTER SEARCH PARTICLE FILTER 296
13.6 APPLYING SSPF TO SOLVE THE DTSP 300
13.7 EXPERIMENTAL RESULTS 301
13.8 CONCLUSIONS 307
REFERENCES 307
14 FROM THE TSP TO THE DYNAMIC VRP: AN APPLICATION O F NEURAL NETWORKS
IN POPULATION BASED METAHEURISTIC 309
AMIR HAJJAM, JEAN-CHARLES CREPUT, AND ABDERRAFIAA KOUKAM 14.1
INTRODUCTION 309
14.2 DYNAMIC EUCLIDEAN VEHICLE ROUTING PROBLEM 312
14.3 METHOD PRINCIPLE 315
14.4 THE METAHEURISTIC EMBEDDING FRAMEWORK 318
14.5 THE EVOLUTIONARY ALGORITHM EMBEDDING SELF-ORGANIZING MAPS 319
14.6 REAL-TIME SIMULATION AND OPTIMIZER 324
14.7 EXPERIMENTAL RESULTS 328
14.8 CONCLUSIONS 336
REFERENCES . 337
15 INSECT SWARM ALGORITHMS FOR DYNAMIC MAX-SAT PROBLEMS 34 1 PEDRO C.
PINTO, THOMAS A. RUNKLER, AND JOAO M.C. SOUSA 15.1 INTRODUCTION 342
15.2 MAX-SAT OPTIMIZATION PROBLEM 343
15.3 THE DYNAMIC MAX-SAT OPTIMIZATION PROBLEM 345
15.4 ACO APPLIED TO MAX-SAT 348
15.5 WSO APPLIED TO MAX-SAT 351
15.6 SA AND GHC APPLIED TO MAX-SAT 353
IMAGE 5
CONTENTS XV
15.7 EXPERIMENTS 355
15.8 ANALYSIS AND RESULTS 357
15.9 CONCLUSIONS 367
REFERENCES 367
16 DYNAMIC TIME-LINKAGE EVOLUTIONARY OPTIMIZATION: DEFINITIONS AND
POTENTIAL SOLUTIONS 37 1
TRUNG THANH NGUYEN AND XIN YAO 16.1 DYNAMIC TIME-LINKAGE PROBLEMS - FROM
ACADEMIC RESEARCH TO REAL-WORLD APPLICATIONS 372
16.2 A FRAMEWORK FOR DEFINING DOPS AND DTPS 373
16.3 THE PREDICTION-DECEPTIVE PROBLEM IN DTPS 380
16.4 CONCLUSIONS 393
REFERENCES 393
INDEX 397
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| isbn | 9783642306648 |
| language | English |
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| physical | XXXI, 398 S. Ill., graph. Darst. |
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| spelling | Metaheuristics for dynamic optimization Enrique Alba ... (eds.) Berlin [u.a.] Springer 2013 XXXI, 398 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence 433 Dynamische Optimierung (DE-588)4125677-3 gnd rswk-swf Metaheuristik (DE-588)4820176-5 gnd rswk-swf Dynamische Optimierung (DE-588)4125677-3 s Metaheuristik (DE-588)4820176-5 s DE-604 Alba, Enrique Sonstige oth Studies in computational intelligenc 433 (DE-604)BV020822171 433 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025291953&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Metaheuristics for dynamic optimization Studies in computational intelligenc Dynamische Optimierung (DE-588)4125677-3 gnd Metaheuristik (DE-588)4820176-5 gnd |
| subject_GND | (DE-588)4125677-3 (DE-588)4820176-5 |
| title | Metaheuristics for dynamic optimization |
| title_auth | Metaheuristics for dynamic optimization |
| title_exact_search | Metaheuristics for dynamic optimization |
| title_full | Metaheuristics for dynamic optimization Enrique Alba ... (eds.) |
| title_fullStr | Metaheuristics for dynamic optimization Enrique Alba ... (eds.) |
| title_full_unstemmed | Metaheuristics for dynamic optimization Enrique Alba ... (eds.) |
| title_short | Metaheuristics for dynamic optimization |
| title_sort | metaheuristics for dynamic optimization |
| topic | Dynamische Optimierung (DE-588)4125677-3 gnd Metaheuristik (DE-588)4820176-5 gnd |
| topic_facet | Dynamische Optimierung Metaheuristik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025291953&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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