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| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boston [u.a.]
Kluwer
2001
|
| Ausgabe: | 4. print. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIX, 382 S. graph. Darst. |
| ISBN: | 079239965X 0792381874 |
Internformat
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Datensatz im Suchindex
| _version_ | 1804128585375547392 |
|---|---|
| adam_text | CONTENTS
PREFACE xvii
1 TABU SEARCH BACKGROUND l
1.1 General Tenets 2
1.2 Use of Memory 4
1.2.1 An Illustrative Preview 5
1.3 Intensification and Diversification 8
1.4 Adaptive Memory Programming 9
1.5 Is Memory Really a Good Idea? 10
1.6 Points of Departure 11
1.7 Elements of Adaptive Memory 11
1.8 Historical Note on Tabu Search 13
1.9 Historical Note on Meta Heuristics 17
1.9.1 Additional Meta Heuristic Features 19
1.9.2 Common Distinctions 20
1.9.3 Metaphors of Nature 22
1.10 Discussion Questions and Exercises 23
2 TS FOUNDATIONS: SHORT TERM MEMORY 25
2.1 Memory and Tabu Classifications 29
2.2 Recency Based Memory 31
2.3 A First Level Tabu Search Approach 36
viii
2.3.1 Critical Event Memory 39
2.4 Recency Based Memory for Add/Drop Moves 42
2.4.1 Some Useful Notation 43
2.4.2 Streamlining 45
2.5 Tabu Tenure 46
2.5.1 Random Dynamic Tenure 48
2.5.2 Systematic Dynamic Tenure 49
2.6 Aspiration Criteria and Regional Dependencies 50
2.7 Discussion Questions and Exercises 54
3 TS FOUNDATIONS: ADDITIONAL ASPECTS OF SHORT
TERM MEMORY 59
3.1 Tabu Search and Candidate List Strategies 59
3.2 Some General Classes of Candidate List Strategies 61
3.2.1 Aspiration Plus 61
3.2.2 Elite Candidate List 63
3.2.3 Successive Filter Strategy 64
3.2.4 Sequential Fan Candidate List 65
3.2.5 Bounded Change Candidate List 67
3.3 Connections Between Candidate Lists, Tabu Status and
Aspiration Criteria 67
3.4 Logical Restructuring 68
3.4.1 Restructuring by Changing Evaluations and Neighborhoods ... 71
3.4.2 Threshold Based Restructuring and Induced Decomposition... 73
3.5 Special Cases and Extensions of Recency Based Implementations 75
3.5.1 Tabu Restrictions: Transforming Attribute Status
into Move Status 76
3.5.2 Applications with Increase/Decrease Moves 79
3.5.3 General Case Increase/Decrease Moves 80
ix
3.5.4 Moves for Permutation Problems Related to
Add/Drop Moves 84
3.6 Discussion Questions and Exercises 86
4 TS FOUNDATIONS: LONGER TERM MEMORY 93
4.1 Frequency Based Approach 94
4.2 Intensification Strategies 96
4.3 Diversification Strategies 98
4.3.1 Modifying Choice Rules 99
4.3.2 Restarting 100
4.4 Strategic Oscillation 102
4.4.1 Strategic Oscillation Patterns and Decisions 104
4.5 Path Relinking Ill
4.5.1 Roles in Intensification and Diversification 114
4.5.2 Incorporating Alternative Neighborhoods 115
4.6 The Intensification / Diversification Distinction 116
4.7 Some Basic Memory Structures for Longer Term Strategies 118
4.7.1 Conventions 118
4.7.2 Frequency Based Memory 119
4.7.3 Critical Event Memory 120
4.8 Discussion Questions and Exercises 122
5 TABU SEARCH PRINCIPLES 125
5.1 Influence and Measures of Distance and Diversity 125
5.1.1 Influential Diversity 127
5.1.2 Influence/Quality Tradeoffs 128
5.2 The Principle of Persistent Attractiveness 132
5.3 The Principle of Persistent Voting 134
5.4 Compound Moves, Variable Depth and Ejection Chains 135
5.5 The Proximate Optimality Principle 138
X
5.6 The Principle of Congenial Structures 141
5.6.1 Congenial Structures Based on Influence 143
5.6.2 Congenial Structures Based on Improving Signatures 144
5.7 The Pyramid Principle 145
5.8 The Space/Time Principle 146
5.9 Discussion Questions and Exercises 148
6 TABU SEARCH IN INTEGER PROGRAMMING 153
6.1 A Tabu Branching Method 154
6.2 Tabu Search and Cut Search 158
6.2.1 Convexity Cut Construction 160
6.3 Cut Search 162
6.4 Star Paths for Integer Programs 174
6.4.1 Main Results and Implications for Zero One Problems 177
6.4.2 Scatter Search and Directional Rounding for Zero One
Problems 181
6.4.3 Special Implications of LP Optimality 182
6.4.4 The Creation of Star Paths 182
6.4.5 Choosing Boundary Points for Creating Star Paths 183
6.4.6 An Efficient Procedure for Generating the Star Paths 185
6.4.7 Adaptive Re Determination of Star Paths Using
Tabu Search Intensification Strategies 189
6.4.8 Strategy for Augmenting a Collection of Preferred Solutions ... 190
6.4.9 Star Paths for General Integer Programs 191
6.4.10 Summary Considerations 193
6.5 Branching on Created Variables 193
6.5.1 A Perspective to Unite Branching and Cutting 195
6.5.2 Straddle Dominance Branching on Created Variables 199
6.5.3 Added Significance of S D Branching 203
6.5.4 Triple Branching 204
xi
6.5.5 Conclusions 208
6.6 Discussion Questions and Exercises 209
7 SPECIAL TABU SEARCH TOPICS 227
7.1 Probabilistic Tabu Search 227
7.2 Tabu Thresholding 231
7.2.1 Candidate Lists and Scanning 234
7.2.2 Candidate Selection 237
7.2.3 Specialized Tabu Thresholding Procedure 237
7.3 Special Dynamic Tabu Tenure Strategies 239
7.3.1 The Reverse Elimination Method 239
7.3.2 Moving Gaps 241
7.3.3 The Tabu Cycle Method 241
7.3.4 Conditional Probability Method 244
7.4 Hash Functions 246
7.5 Ejection Chains 248
7.5.1 Reference Structures and Traveling Salesman Applications .... 250
7.6 Vocabulary Building 252
7.7 Nonlinear and Parametric Optimization 256
7.7.1 Directional Search Strategies 258
7.8 Parallel Processing 260
7.8.1 Taxonomy of Parallel Tabu Search Procedures 263
7.8.2 Parallelism and Probabilistic Tabu Search 265
7.9 Discussion Questions and Exercises 265
8 TABU SEARCH APPLICATIONS 267
8.1 Planning and Scheduling 268
8.1.1 Scheduling in Manufacturing Systems 268
8.1.2 Audit Scheduling 269
8.1.3 Scheduling a Flow Line Manufacturing Cell 269
xii
8.1.4 Generalized Capacity Requirements in Production
Scheduling 270
8.1.5 Production Planning with Workforce Learning 270
8.1.6 Process Plan Optimization for Multi Spindle, Multi Turret
CNC Turning Centers 271
8.1.7 Sustainable Forest Harvesting 272
8.1.8 Forest Harvest Scheduling to Satisfy Green Up Constraints.... 272
8.2 Telecommunications 273
8.2.1 Hub and Spoke Communication Networks 273
8.2.2 Optimization of B ISDN Telecommunication Networks 274
8.2.3 Design of Optical Telecommunication Networks 275
8.3 Parallel Computing 275
8.3.1 Mapping Tasks to Processors to Minimize Communication
Time in a Multiprocessor System 275
8.3.2 Multiprocessor Task Scheduling in Parallel Programs 276
8.3.3 Multiprocessor Task Scheduling Using Parallel Tabu Search .. 276
8.3.4 Quadratic Assignment Problem on a Connection Machine 277
8.3.5 Asynchronous Parallel Tabu Search for Integrated
Circuit Design 277
8.3.6 Asynchronous Multithread Tabu Search Variants 278
8.4 Transportation, Routing and Network Design 278
8.4.1 The Fixed Charge Transportation Problem 278
8.4.2 The Transportation Problem with Exclusionary
Side Constraints 279
8.4.3 The Vehicle Routing Problem 280
8.4.4 Vehicle Routing Problem with Time Windows 280
8.4.5 Routing and Distribution 281
8.4.6 Probabilistic Diversification and Intensification
in Local Search for Vehicle Routing 282
8.4.7 Quadratic Semi Assignment and Mass Transit Applications.... 283
xiii
8.4.8 Automated Guided Vehicle Systems Flowpath
Design Applications 283
8.4.9 Muticommodity Capacitated Fixed Charge Network Design ... 284
8.5 Optimization on Structures 285
8.5.1 Protein Conformation Lattice Model 285
8.5.2 Optimization of Electromagnetic Structures 285
8.5.3 The Damper Placement Problem on Space Truss Structures.... 286
8.5.4 The Polymer Straightening Problem 286
8.5.5 Active Structural Acoustic Control 287
8.6 Optimization on Graphs 287
8.6.1 The P Median Problem 287
8.6.2 Graph Partitioning 288
8.6.3 Determining New Ramsey Graphs 289
8.6.4 The Maximum Clique Problem 289
8.6.5 Graph Drawing 290
8.7 Neural Networks andLearning 292
8.7.1 Sub Symbolic Machine Learning (Neural Networks) 292
8.7.2 Global Optimization with Artificial Neural Networks 293
8.7.3 Neural Networks for Combinatorial Optimization 293
8.7.4 VLSI Systems with Learning Capabilities 294
8.8 Continuous and Stochastic Optimization 295
8.8.1 Continuous Optimization 295
8.8.2 Mixed Integer, Multi Stage Stochastic Programming 295
8.8.3 Portfolio Management 296
8.9 Manufacturing 296
8.9.1 Task Assignment for Assembly Line Balancing 296
8.9.2 Facility Layout in Manufacturing 297
8.9.3 Two Dimensional Irregular Cutting 297
8.10 Financial Analysis 298
8.10.1 Computer Aided Design of Financial Products 298
xiv
8.10.2 Dynamic Investment Problems 299
8.11 Specialized Techniques and General Zero One Solvers 300
8.11.1 Reactive Tabu Search for Combinatorial Optimization 300
8.11.2 Chunking in Tabu Search 301
8.11.3 Zero One Mixed Integer Programming 301
8.12 Constraint Satisfaction and Satisfiability 302
8.12.1 Constraint Satisfaction Problem as General Problem Solver.... 302
8.12.2 Advances for Satisfiability Problems — Surrogate Constraint
Analysis and Tabu Learning 302
9 CONNECTIONS, HYBRID APPROACHES AND
LEARNING 305
9.1 Simulated Annealing 306
9.2 Genetic Algorithms 308
9.2.1 Models of Nature — Beyond Genetic Metaphors 311
9.3 Scatter Search 314
9.3.1 Modern Forms and Applications of Scatter Search 317
9.3.2 Scatter Search and Path Relinking Interconnections 318
9.4 Greedy Randomized Adaptive Search Procedures 319
9.5 Neural Networks 321
9.6 Target Analysis 323
9.6.1 Target Analysis Features 325
9.6.2 Illustrative Application and Implications 328
9.6.3 Conditional Dependencies Among Attributes 331
9.6.4 Differentiating Among Targets 332
9.6.5 Generating Rules by Optimization Models 332
9.7 Discussion Questions and Exercises 333
9.8 Appendix: Illustrative Version of Nonlinear Scatter Search 335
9.8.1 The Method in Overview 336
9.8.2 Steps of the Nonlinear Scatter Search 336
XV
9.8.3 Generating Trial Points 337
9.8.4 Tabu Restrictions and Aspiration Criteria 339
9.8.5 Refinements 340
9.8.6 Parallel Processing Implementation 342
10 NEGLECTED TABU SEARCH STRATEGIES 343
10.1 Candidate List Strategies 343
10.2 Probabilistic Tabu Search 344
10.3 Intensification Approaches 345
10.3.1 Restarting with Elite Solutions 345
10.3.2 Frequency of Elite Solutions 345
10.3.3 Memory and Intensification 346
10.3.4 Relevance of Clustering for Intensification 347
10.4 Diversification Approaches 347
10.4.1 Diversification and Intensification Links 348
10.4.2 Implicit Conflict and the Importance of Interactions 349
10.4.3 Reactive Tabu Search 349
10.4.4 Ejection Chain Approaches 350
10.5 Strategic Oscillation 351
10.6 Clustering and Conditional Analysis 352
10.6.1 Conditional Relationships 353
10.7 Referent Domain Optimization 354
10.8 Discussion Questions and Exercises 356
REFERENCES 359
INDEX 377
|
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| id | DE-604.BV013766104 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:51:35Z |
| institution | BVB |
| isbn | 079239965X 0792381874 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-009409533 |
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| spelling | Glover, Fred 1937- Verfasser (DE-588)135961424 aut Tabu search Fred Glover ; Manuel Laguna 4. print. Boston [u.a.] Kluwer 2001 XIX, 382 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Künstliche Intelligenz Artificial intelligence Mathematical optimization Operations research Optimierung (DE-588)4043664-0 gnd rswk-swf Tabusuche (DE-588)4432514-9 gnd rswk-swf Optimierung (DE-588)4043664-0 s Tabusuche (DE-588)4432514-9 s DE-604 Laguna, Manuel Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009409533&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Glover, Fred 1937- Laguna, Manuel Tabu search Künstliche Intelligenz Artificial intelligence Mathematical optimization Operations research Optimierung (DE-588)4043664-0 gnd Tabusuche (DE-588)4432514-9 gnd |
| subject_GND | (DE-588)4043664-0 (DE-588)4432514-9 |
| title | Tabu search |
| title_auth | Tabu search |
| title_exact_search | Tabu search |
| title_full | Tabu search Fred Glover ; Manuel Laguna |
| title_fullStr | Tabu search Fred Glover ; Manuel Laguna |
| title_full_unstemmed | Tabu search Fred Glover ; Manuel Laguna |
| title_short | Tabu search |
| title_sort | tabu search |
| topic | Künstliche Intelligenz Artificial intelligence Mathematical optimization Operations research Optimierung (DE-588)4043664-0 gnd Tabusuche (DE-588)4432514-9 gnd |
| topic_facet | Künstliche Intelligenz Artificial intelligence Mathematical optimization Operations research Optimierung Tabusuche |
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