Metaheuristics: from design to implementation
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Hoboken, N.J.
Wiley
2009
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XXIX, 593 p. graph. Darst. |
| ISBN: | 9780470278581 |
Staff View
MARC
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| 008 | 090715s2009 xxud||| |||| 00||| eng d | ||
| 010 | |a 2009017331 | ||
| 020 | |a 9780470278581 |c cloth |9 978-0-470-27858-1 | ||
| 035 | |a (OCoLC)230183356 | ||
| 035 | |a (DE-599)BVBBV035629019 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 044 | |a xxu |c US | ||
| 049 | |a DE-703 |a DE-739 |a DE-384 |a DE-11 | ||
| 050 | 0 | |a QA402.5 | |
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| 084 | |a QH 400 |0 (DE-625)141571: |2 rvk | ||
| 084 | |a SK 890 |0 (DE-625)143267: |2 rvk | ||
| 100 | 1 | |a Talbi, El-Ghazali |d 1965- |e Verfasser |0 (DE-588)131576542 |4 aut | |
| 245 | 1 | 0 | |a Metaheuristics |b from design to implementation |c El-Ghazali Talbi |
| 264 | 1 | |a Hoboken, N.J. |b Wiley |c 2009 | |
| 300 | |a XXIX, 593 p. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Mathematical optimization | |
| 650 | 4 | |a Heuristic programming | |
| 650 | 4 | |a Problem solving |x Data processing | |
| 650 | 4 | |a Computer algorithms | |
| 650 | 0 | 7 | |a Metaheuristik |0 (DE-588)4820176-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Metaheuristik |0 (DE-588)4820176-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017684007&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-017684007 | ||
Record in the Search Index
| _version_ | 1804139304050491392 |
|---|---|
| adam_text | CONTENTS
Preface
xvii
Acknowledgments
xxiii
Glossary
xxv
1
Common Concepts for
M
etaheuristics
1
1.1
Optimization Models
2
1.1.1
Classical Optimization Models
3
1.1.2
Complexity Theory
9
1.1.2.1
Complexity of Algorithms
9
1.1.2.2
Complexity of Problems
11
1.2
Other Models for Optimization
14
1.2.1
Optimization Under Uncertainty
15
1.2.2
Dynamic Optimization
16
1.2.2.1
Multiperiodic Optimization
16
1.2.3
Robust Optimization
17
1.3
Optimization Methods
18
1.3.1
Exact Methods
19
1.3.2
Approximate Algorithms
21
1.3.2.1
Approximation Algorithms
21
1.3.3
Metaheuristics
23
1.3.4
Greedy Algorithms
26
1.3.5
When Using Metaheuristics?
29
1.4
Main Common Concepts for Metaheuristics
34
1.4.1
Representation
34
1.4.1.1
Linear Representations
36
1.4.1.2
Nonlinear Representations
39
1.4.1.3
Representation-Solution Mapping
40
1.4.1.4
Direct Versus Indirect Encodings
41
1.4.2
Objective Function
43
1.4.2.1
Self-Sufficient Objective Functions
43
vii
Vili
CONTENTS
1.4.2.2
Guiding Objective Functions
44
1.4.2.3
Representation Decoding
45
1.4.2.4
Interactive Optimization
46
1.4.2.5
Relative and Competitive Objective Functions
47
1.4.2.6
Meta-Modeling
47
1.5
Constraint Handling
48
1.5.1
Reject Strategies
49
1.5.2
Penalizing Strategies
49
1.5.3
Repairing Strategies
52
1.5.4
Decoding Strategies
53
1.5.5
Preserving Strategies
53
1.6
Parameter Tuning
54
1.6.1
Off-Line Parameter Initialization
54
1.6.2
Online Parameter Initialization
56
1.7
Performance Analysis of Metaheuristics
57
1.7.1
Experimental Design
57
1.7.2
Measurement
60
1.7.2.1
Quality of Solutions
60
1.7.2.2
Computational Effort
62
1.7.2.3
Robustness
62
1.7.2.4
Statistical Analysis
63
1.7.2.5
Ordinal Data Analysis
64
1.7.3
Reporting
65
1.8
Software Frameworks for Metaheuristics
67
1.8.1
Why a Software Framework for Metaheuristics?
67
1.8.2
Main Characteristics of Software Frameworks
69
1.8.3
ParadisEO Framework
71
1.8.3.1
ParadisEO Architecture
74
1.9
Conclusions
76
1.10
Exercises
79
2
Single-Solution Based Metaheuristics
87
2.1
Common Concepts for Single-Solution Based Metaheuristics
87
2.1.1
Neighborhood
88
2.1.2
Very Large Neighborhoods
94
2.1.2.1
Heuristic Search in Large Neighborhoods
95
CONTENTS
IX
2.1.2.2
Exact Search in Large Neighborhoods
98
2.1.2.3
Polynomial-Specific Neighborhoods
100
2.1.3
Initial Solution
101
2.1.4
Incremental Evaluation of the Neighborhood
102
2.2
Fitness Landscape Analysis
103
2.2.1
Distances in the Search Space
106
2.2.2
Landscape Properties
108
2.2.2.1
Distribution Measures
109
2.2.2.2
Correlation Measures 111
2.2.3
Breaking Plateaus in a Rat Landscape
119
2.3
Local Search
121
2.3.1
Selection of the Neighbor
123
2.3.2
Escaping from Local Optima
125
2.4
Simulated Annealing
126
2.4.1
Move Acceptance
129
2.4.2
Cooling Schedule
130
2.4.2.1
Initial Temperature
130
2.4.2.2
Equilibrium State
131
2.4.2.3
Cooling
131
2.4.2.4
Stopping Condition
133
2.4.3
Other Similar Methods
133
2.4.3.1
Threshold Accepting
133
2.4.3.2
Record-to-Record Travel
137
2.4.3.3
Great Deluge Algorithm
137
2.4.3.4
Demon Algorithms
138
2.5
Tabu Search
140
2.5.1
Short-Term Memory
142
2.5.2
Medium-Term Memory
144
2.5.3
Long-Term
Memory
145
2.6
Iterated Local Search
146
2.6.1
Perturbation Method
148
2.6.2
Acceptance Criteria
149
2.7
Variable Neighborhood Search
150
2.7.1
Variable Neighborhood Descent
150
2.7.2
General Variable Neighborhood Search
151
2.8
Guided Local Search
154
X
CONTENTS
2.9
Other Single-Solution Based
Metaheuristics 157
2.9.1
Smoothing Methods
157
2.9.2
Noisy Method
160
2.9.3
GRASP
164
2.10
S-Metaheuristic Implementation Under ParadisEO
168
2.10.1
Common Templates for Metaheuristics
169
2.10.2
Common Templates for S-Metaheuristics
170
2.10.3
Local Search Template
170
2.10.4
Simulated Annealing Template
172
2.10.5
Tabu Search Template
173
2.10.6
Iterated Local Search Template
175
2.11
Conclusions
177
2.12
Exercises
180
3
Population-Based Metaheuristics
190
3.1
Common Concepts for Population-Based Metaheuristics
191
3.1.1
Initial Population
193
3.1.1.1
Random Generation
194
3.1.1.2
Sequential Diversification
195
3.1.1.3
Parallel Diversification
195
3.1.1.4
Heuristic Initialization
198
3.1.2
Stopping Criteria
198
3.2
Evolutionary Algorithms
199
3.2.1
Genetic Algorithms
201
3.2.2
Evolution Strategies
202
3.2.3
Evolutionary Programming
203
3.2.4
Genetic Programming
203
3.3
Common Concepts for Evolutionary Algorithms
205
3.3.1
Selection Methods
206
3.3.1.1
Roulette Wheel Selection
206
3.3.1.2
Stochastic Universal Sampling
206
3.3.1.3
Tournament Selection
207
3.3.1.4
Rank-Based Selection
207
3.3.2
Reproduction
208
3.3.2.1
Mutation
208
3.3.2.2
Recombination or Crossover
213
3.3.3
Replacement Strategies
221
CONTENTS
X¡
3.4
Other Evolutionary Algorithms
221
3.4.1
Estimation of Distribution Algorithms
222
3.4.2
Differential Evolution
225
3.4.3
Coevolutionary Algorithms
228
3.4.4
Cultural Algorithms
232
3.5
Scatter Search
233
3.5.1
Path Relinking
237
3.6
Swarm Intelligence
240
3.6.1
Ant Colony Optimization Algorithms
240
3.6.1.1
АСО
for Continuous Optimization Problems
247
3.6.2
Particle Swarm Optimization
247
3.6.2.1
Particles Neighborhood
248
3.6.2.2
PSO for Discrete Problems
252
3.7
Other Population-Based Methods
255
3.7.1
Bees Colony
255
3.7.1.1
Bees in Nature
255
3.7.1.2
Nest Site Selection
256
3.7.1.3
Food Foraging
257
3.7.1.4
Marriage Process
262
3.7.2
Artificial Immune Systems
264
3.7.2.1
Natural Immune System
264
3.7.2.2
Clonal Selection Theory
265
3.7.2.3
Negative Selection Principle
268
3.7.2.4
Immune Network Theory
268
3.7.2.5
Danger Theory
269
3.8
P-metaheuristics Implementation Under ParadisEO
270
3.8.1
Common Components and Programming Hints
270
3.8.1.1
Main Core Templates—ParadisEO-EO s Functors
270
3.8.1.2
Representation
272
3.8.2
Fitness Function
274
3.8.2.1
Initialization
Ti
4
3.8.2.2
Stopping Criteria, Checkpoints, and Statistics
275
3.8.2.3
Dynamic Parameter Management and State
Loader/Register
277
3.8.3
Evolutionary Algorithms Under ParadisEO
278
3.8.3.1
Representation
278
3.8.3.2
Initialization
279
3.8.3.3
Evaluation
279
XÜ CONTENTS
3.8.3.4 Variation Operators 279
3.8.3.5 Evolution Engine 283
3.8.3.6
Evolutionary Algorithms
285
3.8.4
Particle Swarm Optimization Under ParadisEO
286
3.8.4.1
Illustrative Example
292
3.8.5
Estimation of Distribution Algorithm Under ParadisEO
293
3.9
Conclusions
294
3.10
Exercises
296
4
M
etaheuristics for Multiobjective Optimization
308
4.1
Multiobjective Optimization Concepts
310
4.2
Multiobjective Optimization Problems
315
4.2.1
Academic Applications
316
4.2.1.1
Multiobjective Continuous Problems
316
4.2.1.2
Multiobjective Combinatorial Problems
317
4.2.2
Real-Life Applications
318
4.2.3
Multicriteria Decision Making
320
4.3
Main Design Issues of Multiobjective Metaheuristics
322
4.4
Fitness Assignment Strategies
323
4.4.1
Scalar Approaches
324
4.4.1.1
Aggregation Method
324
4.4.1.2
Weighted Metrics
327
4.4.1.3
Goal Programming
330
4.4.1.4
Achievement Functions
330
4.4.1.5
Goal Attainment
330
4.4.1.6
е
-Constraint Method
332
4.4.2
Criterion-Based Methods
334
4.4.2.1
Parallel Approach
334
4.4.2.2
Sequential or Lexicographic Approach
335
4.4.3
Dominance-Based Approaches
337
4.4.4
Indicator-Based Approaches
341
4.5
Diversity Preservation
343
4.5.1
Kernel Methods
344
4.5.2
Nearest-Neighbor Methods
346
4.5.3
Histograms
347
4.6
Elitism
347
CONTENTS
ХІІІ
4.7 Performance Evaluation and Pareto Front
Structure
350
4.7.1 Performance
Indicators
350
4.7.1.1
Convergence-Based Indicators
352
4.7.1.2
Diversity-Based Indicators
354
4.7.1.3
Hybrid Indicators
355
4.7.2
Landscape Analysis of Pareto Structures
358
4.8
Multiobjective Metaheuristics Under ParadisEO
361
4.8.1
Software Frameworks for Multiobjective Metaheuristics
362
4.8.2
Common Components
363
4.8.2.1
Representation
363
4.8.2.2
Fitness Assignment Schemes
364
4.8.2.3
Diversity Assignment Schemes
366
4.8.2.4
Elitism
367
4.8.2.5
Statistical Tools
367
4.8.3
Multiobjective EAs-Related Components
368
4.8.3.1
Selection Schemes
369
4.8.3.2
Replacement Schemes
370
4.8.3.3
Multiobjective Evolutionary Algorithms
371
4.9
Conclusions and Perspectives
373
4.10
Exercises
375
5
Hybrid Metaheuristics
385
5.1
Hybrid Metaheuristics
386
5.1.1
Design Issues
386
5.1.1.1
Hierarchical Classification
386
5.1.1.2
Flat Classification
394
5.1.2
Implementation Issues
399
5.1.2.1
Dedicated Versus General-Purpose Computers
399
5.1.2.2
Sequential Versus Parallel
399
5.1.3
A Grammar for Extended Hybridization Schemes
400
5.2
Combining Metaheuristics with Mathematical Programming
401
5.2.1
Mathematical Programming Approaches
402
5.2.1.1
Enumerative Algorithms
402
5.2.1.2
Relaxation and Decomposition Methods
405
5.2.1.3
Branch and Cut and Price Algorithms
407
5.2.2
Classical Hybrid Approaches
407
5.2.2.1
Low-Level Relay Hybrids
408
5.2.2.2
Low-Level Teamwork Hybrids
411
XIV
CONTENTS
5.2.2.3 High-Level
Relay Hybrids
413
5.2.2.4 High-Level Teamwork
Hybrids
416
5.3
Combining
Metaheuristics
with Constraint Programming
418
5.3.1
Constraint Programming
418
5.3.2
Classical Hybrid Approaches
419
5.3.2.1
Low-Level Relay Hybrids
420
5.3.2.2
Low-Level Teamwork Hybrids
420
5.3.2.3
High-Level Relay Hybrids
422
5.3.2.4
High-Level Teamwork Hybrids
422
5.4
Hybrid Metaheuristics with Machine Learning and Data Mining
423
5.4.1
Data Mining Techniques
423
5.4.2
Main Schemes of Hybridization
425
5.4.2.1
Low-Level Relay Hybrid
425
5.4.2.2
Low-Level Teamwork Hybrids
426
5.4.2.3
High-Level Relay Hybrid
428
5.4.2.4
High-Level Teamwork Hybrid
431
5.5
Hybrid Metaheuristics for Multiobjective Optimization
432
5.5.1
Combining Metaheuristics for MOPs
432
5.5.1.1
Low-Level Relay Hybrids
432
5.5.1.2
Low-Level Teamwork Hybrids
433
5.5.1.3
High-Level Relay Hybrids
434
5.5.1.4
High-Level Teamwork Hybrid
436
5.5.2
Combining Metaheuristics with Exact Methods for MOP
438
5.5.3
Combining Metaheuristics with Data Mining for MOP
444
5.6
Hybrid Metaheuristics Under ParadisEO
448
5.6.1
Low-Level Hybrids Under ParadisEO
448
5.6.2
High-Level Hybrids Under ParadisEO
451
5.6.3
Coupling with Exact Algorithms
451
5.7
Conclusions and Perspectives
452
5.8
Exercises
454
6
Parallel Metaheuristics
460
6.1
Parallel Design of Metaheuristics
462
6.1.1
Algorithmic-Level Parallel Model
463
6.1.1.1
Independent Algorithmic-Level Parallel Model
463
6.1.1.2
Cooperative Algorithmic-Level Parallel Model
465
CONTENTS
XV
6.1.2
Iteration-Level Parallel Model
471
6.1.2.1
Iteration-Level Model for S-Metaheuristics
471
6.1.2.2
Iteration-Level Model for P-Metaheuristics
472
6.1.3
Solution-Level Parallel Model
476
6.1.4
Hierarchical Combination of the Parallel Models
478
6.2
Parallel Implementation of Metaheuristics
478
6.2.1
Parallel and Distributed Architectures
480
6.2.2
Dedicated Architectures
486
6.2.3
Parallel Programming Environments and Middlewares
488
6.2.4
Performance Evaluation
493
6.2.5
Main Properties of Parallel Metaheuristics
496
6.2.6
Algorithmic-Level Parallel Model
498
6.2.7
Iteration-Level Parallel Model
500
6.2.8
Solution-Level Parallel Model
502
6.3
Parallel Metaheuristics for Multiobjective Optimization
504
6.3.1
Algorithmic-Level Parallel Model for MOP
505
6.3.2
Iteration-Level Parallel Model for MOP
507
6.3.3
Solution-Level Parallel Model for MOP
507
6.3.4
Hierarchical Parallel Model for MOP
509
6.4
Parallel Metaheuristics Under ParadisEO
512
6.4.1
Parallel Frameworks for Metaheuristics
512
6.4.2
Design of Algorithmic-Level Parallel Models
513
6.4.2.1
Algorithms and Transferred Data (What?)
514
6.4.2.2
Transfer Control (When?)
514
6.4.2.3
Exchange Topology (Where?)
515
6.4.2.4
Replacement Strategy (How?)
517
6.4.2.5
Parallel Implementation
517
6.4.2.6
A Generic Example
518
6.4.2.7
Island Model of EAs Within ParadisEO
519
6.4.3
Design of Iteration-Level Parallel Models
521
6.4.3.1
The Generic Multistart Paradigm
521
6.4.3.2
Use of the Iteration-Level Model
523
6.4.4
Design of Solution-Level Parallel Models
524
6.4.5
Implementation of Sequential Metaheuristics
524
6.4.6
Implementation of Parallel and Distributed Algorithms
525
6.4.7
Deployment of ParadisEO-PEO
528
6.5
Conclusions and Perspectives
529
6.6
Exercises
531
XV¡
CONTENTS
Appendix: UML and
C++ 535
Α.
1
A Brief Overview of UML Notations
535
A.2 A Brief Overview of the
C++
Template Concept
536
References
539
Index
587
|
| any_adam_object | 1 |
| author | Talbi, El-Ghazali 1965- |
| author_GND | (DE-588)131576542 |
| author_facet | Talbi, El-Ghazali 1965- |
| author_role | aut |
| author_sort | Talbi, El-Ghazali 1965- |
| author_variant | e g t egt |
| building | Verbundindex |
| bvnumber | BV035629019 |
| callnumber-first | Q - Science |
| callnumber-label | QA402 |
| callnumber-raw | QA402.5 |
| callnumber-search | QA402.5 |
| callnumber-sort | QA 3402.5 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | QH 400 SK 890 |
| ctrlnum | (OCoLC)230183356 (DE-599)BVBBV035629019 |
| dewey-full | 519.6 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.6 |
| dewey-search | 519.6 |
| dewey-sort | 3519.6 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV035629019 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:41:57Z |
| institution | BVB |
| isbn | 9780470278581 |
| language | English |
| lccn | 2009017331 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017684007 |
| oclc_num | 230183356 |
| open_access_boolean | |
| owner | DE-703 DE-739 DE-384 DE-11 |
| owner_facet | DE-703 DE-739 DE-384 DE-11 |
| physical | XXIX, 593 p. graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Wiley |
| record_format | marc |
| spelling | Talbi, El-Ghazali 1965- Verfasser (DE-588)131576542 aut Metaheuristics from design to implementation El-Ghazali Talbi Hoboken, N.J. Wiley 2009 XXIX, 593 p. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Datenverarbeitung Mathematical optimization Heuristic programming Problem solving Data processing Computer algorithms Metaheuristik (DE-588)4820176-5 gnd rswk-swf Metaheuristik (DE-588)4820176-5 s DE-604 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017684007&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Talbi, El-Ghazali 1965- Metaheuristics from design to implementation Datenverarbeitung Mathematical optimization Heuristic programming Problem solving Data processing Computer algorithms Metaheuristik (DE-588)4820176-5 gnd |
| subject_GND | (DE-588)4820176-5 |
| title | Metaheuristics from design to implementation |
| title_auth | Metaheuristics from design to implementation |
| title_exact_search | Metaheuristics from design to implementation |
| title_full | Metaheuristics from design to implementation El-Ghazali Talbi |
| title_fullStr | Metaheuristics from design to implementation El-Ghazali Talbi |
| title_full_unstemmed | Metaheuristics from design to implementation El-Ghazali Talbi |
| title_short | Metaheuristics |
| title_sort | metaheuristics from design to implementation |
| title_sub | from design to implementation |
| topic | Datenverarbeitung Mathematical optimization Heuristic programming Problem solving Data processing Computer algorithms Metaheuristik (DE-588)4820176-5 gnd |
| topic_facet | Datenverarbeitung Mathematical optimization Heuristic programming Problem solving Data processing Computer algorithms Metaheuristik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017684007&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT talbielghazali metaheuristicsfromdesigntoimplementation |