Search and optimization by metaheuristics: techniques and algorithms inspired by nature
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| Main Authors: | , |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
[Basel]
Birkhäuser
[2016]
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| Subjects: | |
| Online Access: | BTU01 FHR01 FRO01 TUM01 UBM01 UBT01 UBW01 UEI01 UPA01 Volltext Inhaltsverzeichnis |
| Physical Description: | 1 Online-Ressource (XXI, 434 Seiten, 68 illus., 40 illus. in color) |
| ISBN: | 9783319411927 |
| DOI: | 10.1007/978-3-319-41192-7 |
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Record in the Search Index
| _version_ | 1804176492359319552 |
|---|---|
| adam_text | Titel: Search and optimization by metaheuristics
Autor: Du, Ke-Lin
Jahr: 2016
Contents
1 Introduction................................................................................1
1.1 Computation Inspired by Nature..........................................1
1.2 Biological Processes..........................................................3
1.3 Evolution Versus Learning..................................................5
1.4 Swarm Intelligence............................................................6
1.4.1 Group Behaviors....................................................7
1.4.2 Foraging Theory....................................................8
1.5 Heuristics, Metaheuristics, and Hyper-Heuristics....................9
1.6 Optimization......................................................................11
1.6.1 Lagrange Multiplier Method....................................12
1.6.2 Direction-Based Search and Simplex Search..............13
1.6.3 Discrete Optimization Problems..............................14
1.6.4 P, NP, NP-Hard, and NP-Complete..........................16
1.6.5 Multiobjective Optimization Problem........................17
1.6.6 Robust Optimization..............................................19
1.7 Performance Indicators........................................................20
1.8 No Free Lunch Theorem....................................................22
1.9 Outline of the Book............................................................23
References....................................................................................25
2 Simulated Annealing....................................................................29
2.1 Introduction......................................................................29
2.2 Basic Simulated Annealing..................................................30
2.3 Variants of Simulated Annealing..........................................33
References....................................................................................35
3 Genetic Algorithms......................................................................37
3.1 Introduction to Evolutionary Computation.............. 37
3.1.1 Evolutionary Algorithms Versus Simulated
Annealing............................................................39
3.2 Terminologies of Evolutionary Computation..........................39
3.3 Encoding/Decoding............................................................42
3.4 Selection/Reproduction............................ 43
3.5 Crossover..........................................................................46
xi
Xii Contents
3.6 Mutation..........................................................................48
3.7 Noncanonical Genetic Operators..........................................49
3.8 Exploitation Versus Exploration..........................................51
3.9 Two-Dimensional Genetic Algorithms..................................55
3.10 Real-Coded Genetic Algorithms..........................................56
3.11 Genetic Algorithms for Sequence Optimization......................60
References....................................................................................64
4 Genetic Programming..................................................................71
4.1 Introduction......................................................................71
4.2 Syntax Trees......................................................................72
4.3 Causes of Bloat..................................................................75
4.4 Bloat Control....................................................................76
4.4.1 Limiting on Program Size......................................77
4.4.2 Penalizing the Fitness of an Individual
with Large Size......................................................77
4.4.3 Designing Genetic Operators..................................77
4.5 Gene Expression Programming............................................78
References....................................................................................80
5 Evolutionary Strategies................................................................83
5.1 Introduction......................................................................83
5.2 Basic Algorithm................................................................84
5.3 Evolutionary Gradient Search and Gradient Evolution............85
5.4 CMA Evolutionary Strategies..............................................88
References....................................................................................90
6 Differential Evolution..................................................................93
6.1 Introduction......................................................................93
6.2 DE Algorithm....................................................................94
6.3 Variants of DE..................................................................97
6.4 Binary DE Algorithms........................................................100
6.5 Theoretical Analysis on DE................................................100
References....................................................................................101
7 Estimation of Distribution Algorithms..........................................105
7.1 Introduction......................................................................105
7.2 EDA Flowchart..................................................................107
7.3 Population-Based Incremental Learning................................108
7.4 Compact Genetic Algorithms..............................................110
7.5 Bayesian Optimization Algorithm........................................112
7.6 Concergence Properties......................................................112
7.7 Other ED As......................................................................113
7.7.1 Probabilistic Model Building GP..............................115
References....................................................................................116
Contents xiii
8 Topics in Evolutinary Algorithms................................................121
8.1 Convergence of Evolutinary Algorithms................................121
8.1.1 Schema Theorem and Building-Block Hypothesis . . . 121
8.1.2 Finite and Infinite Population Models......................123
8.2 Random Problems and Deceptive Functions..........................125
8.3 Parallel Evolutionary Algorithms..........................................127
8.3.1 Master-Slave Model..............................................129
8.3.2 Island Model........................................................130
8.3.3 Cellular EAs..........................................................132
8.3.4 Cooperative Coevolution........................................133
8.3.5 Cloud Computing..................................................134
8.3.6 GPU Computing....................................................135
8.4 Coevolution......................................................................136
8.4.1 Coevolutionary Approaches....................................137
8.4.2 Coevolutionary Approach for Minimax
Optimization..........................................................138
8.5 Interactive Evolutionary Computation..................................139
8.6 Fitness Approximation........................................................139
8.7 Other Heredity-Based Algorithms........................................141
8.8 Application: Optimizating Neural Networks..........................142
References....................................................................................146
9 Particle Swarm Optimization........................................................153
9.1 Introduction......................................................................153
9.2 Basic PSO Algorithms........................................................154
9.2.1 Bare-Bones PSO....................................................156
9.2.2 PSO Variants Using Gaussian or Cauchy
Distribution..........................................................157
9.2.3 Stability Analysis of PSO........................................157
9.3 PSO Variants Using Different Neighborhood Topologies .... 159
9.4 Other PSO Variants............................................................160
9.5 PSO and EAs: Hybridization..............................................164
9.6 Discrete PSO....................................................................165
9.7 Multi-swarm PSOs............................................................166
References....................................................................................169
10 Artificial Immune Systems..........................................................175
10.1 Introduction......................................................................175
10.2 Immunological Theories......................................................177
10.3 Immune Algorithms............................................................180
10.3.1 Clonal Selection Algorithm....................................180
10.3.2 Artificial Immune Network......................................184
10.3.3 Negative Selection Algorithm..................................185
10.3.4 Dendritic Cell Algorithm........................................186
References....................................................................................187
Contents
11 Ant Colony Optimization 191
11.1 Introduction 191
11.2 Ant-Colony Optimization 192
11.2.1 Basic ACO Algorithm 194
11.2.2 ACO for Continuous Optimization 195
References 198
12 Bee Metaheuristics 201
12.1 Introduction 201
12.2 Artificial Bee Colony Algorithm 203
12.2.1 Algorithm Flowchart 203
12.2.2 Modifications on ABC Algorithm 207
12.2.3 Discrete ABC Algorithms 208
12.3 Marriage in Honeybees Optimization 209
12.4 Bee Colony Optimization 210
12.5 Other Bee Algorithms 211
12.5.1 Wasp Swarm Optimization 212
References 213
13 Bacterial Foraging Algorithm 217
13.1 Introduction 217
13.2 Bacterial Foraging Algorithm 219
13.3 Algorithms Inspired by Molds, Algae, and Tumor Cells 222
References 224
14 Harmony Search 227
14.1 Introduction 227
14.2 Harmony Search Algorithm 228
14.3 Variants of Harmony Search 230
14.4 Melody Search 233
References 234
15 Swarm Intelligence 237
15.1 Glowworm-Based Optimization 237
15.1.1 Glowworm Swarm Optimization 238
15.1.2 Firefly Algorithm 239
15.2 Group Search Optimization 240
15.3 Shuffled Frog Leaping 241
15.4 Collective Animal Search 242
15.5 Cuckoo Search 243
15.6 Bat Algorithm 246
15.7 Swarm Intelligence Inspired by Animal Behaviors 247
15.7.1 Social Spider Optimization 247
15.7.2 Fish Swarm Optimization 249
15.7.3 Krill Herd Algorithm 250
15.7.4 Cockroach-Based Optimization 251
15.7.5 Seven-Spot Ladybird Optimization 252
Contents xv
15.7.6 Monkey-Inspired Optimization................................252
15.7.7 Migrating-Based Algorithms....................................253
15.7.8 Other Methods......................................................254
15.8 Plant-Based Metaheuristics..................................................255
15.9 Other Swarm Intelligence-Based Metaheuristics......................257
References....................................................................................259
16 Biomolecular Computing..............................................................265
16.1 Introduction......................................................................265
16.1.1 Biochemical Networks............................................267
16.2 DNA Computing................................................................268
16.2.1 DNA Data Embedding............................................271
16.3 Membrane Computing........................................................271
16.3.1 Cell-Like P System................................................272
16.3.2 Computing by P System........................................273
16.3.3 Other P Systems....................................................275
16.3.4 Membrane-Based Optimization................................277
References....................................................................................278
17 Quantum Computing..................................................................283
17.1 Introduction......................................................................283
17.2 Fundamentals....................................................................284
17.2.1 Graver s Search Algorithm......................................286
17.3 Hybrid Methods................................................................287
17.3.1 Quantum-Inspired EAs............................................287
17.3.2 Other Quantum-Inspired Hybrid Algorithms..............290
References....................................................................................291
18 Metaheuristics Based on Sciences................................................295
18.1 Search Based on Newton s Laws..........................................295
18.2 Search Based on Electromagnetic Laws................................297
18.3 Search Based on Thermal-Energy Principles..........................298
18.4 Search Based on Natural Phenomena....................................299
18.4.1 Search Based on Water Flows................................299
18.4.2 Search Based on Cosmology..................................301
18.4.3 Black Hole-Based Optimization..............................302
18.5 Sorting..............................................................................303
18.6 Algorithmic Chemistries......................................................304
18.6.1 Chemical Reaction Optimization..............................304
18.7 Biogeography-Based Optimization........................................306
18.8 Methods Based on Mathematical Concepts............................309
18.8.1 Opposition-Based Learning......................................310
References....................................................................................311
19 Memetic Algorithms....................................................................315
19.1 Introduction......................................................................315
19.2 Cultural Algorithms............................................................316
Contents
19.3 Memetic Algorithms..........................................................318
19.3.1 Simplex-based Memetic Algorithms..........................320
19.4 Application: Searching Low Autocorrelation Sequences..........321
References....................................................................................324
20 Tabu Search and Scatter Search..................................................327
20.1 Tabu Search......................................................................327
20.1.1 Iterative Tabu Search..............................................330
20.2 Scatter Search....................................................................331
20.3 Path Relinking..................................................................333
References....................................................................................335
21 Search Based on Human Behaviors..............................................337
21.1 Seeker Optimization Algorithm............................................337
21.2 Teaching-Learning-Based Optimization................................338
21.3 Imperialist Competitive Algorithm........................................340
21.4 Several Metaheuristics Inspired by Human Behaviors............342
References....................................................................................345
22 Dynamic, Multimodal, and Constrained Optimizations..................347
22.1 Dynamic Optimization........................................................347
22.1.1 Memory Scheme....................................................348
22.1.2 Diversity Maintaining or Reinforcing........................348
22.1.3 Multiple Population Scheme....................................349
22.2 Multimodal Optimization....................................................350
22.2.1 Crowding and Restricted Tournament Selection .... 351
22.2.2 Fitness Sharing......................................................353
22.2.3 Speciation............................................................354
22.2.4 Clearing, Local Selection, and Demes......................356
22.2.5 Other Methods......................................................357
22.2.6 Metrics for Multimodal Optimization........................359
22.3 Constrained Optimization....................................................359
22.3.1 Penalty Function Method........................................360
22.3.2 Using Multiobjective Optimization Techniques..........363
References....................................................................................365
23 Multiobjective Optimization........................................................371
23.1 Introduction......................................................................371
23.2 Multiobjective Evolutionary Algorithms................................373
23.2.1 Nondominated Sorting Genetic Algorithm II..............374
23.2.2 Strength Pareto Evolutionary Algorithm 2................377
23.2.3 Pareto Archived Evolution Strategy (PAES)..............378
23.2.4 Pareto Envelope-Based Selection Algorithm..............379
23.2.5 MOEA Based on Decomposition (MOEA/D)............380
23.2.6 Several MOEAs....................................................381
Contents _____ __________ ________ ____ xvii
23.2.7 Nondominated Sorting............................................384
23.2.8 Multiobjective Optimization
Based on Differential Evolution..............................385
23.3 Performance Metrics..........................................................386
23.4 Many-Objective Optimization..............................................389
23.4.1 Challenges in Many-Objective Optimization..............389
23.4.2 Pareto-Based Algorithms........................................391
23.4.3 Decomposition-Based Algorithms............................393
23.5 Multiobjective Immune Algorithms......................................394
23.6 Multiobjective PSO............................................................395
23.7 Multiobjective EDAs..........................................................398
23.8 Tabu/Scatter Search Based Multiobjective Optimization..........399
23.9 Other Methods..................................................................400
23.10 Coevolutionary MOEAs......................................................402
References....................................................................................403
Appendix A: Benchmarks..................................................................413
Index................................................................................................431
|
| any_adam_object | 1 |
| author | Du, Ke-Lin Swamy, M. N. S. 1935- |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Mathematik Wirtschaftswissenschaften |
| doi_str_mv | 10.1007/978-3-319-41192-7 |
| format | Electronic eBook |
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| id | DE-604.BV043706556 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:33:02Z |
| institution | BVB |
| isbn | 9783319411927 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029118869 |
| oclc_num | 955034705 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM DE-19 DE-BY-UBM DE-20 DE-739 DE-634 DE-898 DE-BY-UBR DE-861 DE-703 DE-824 DE-83 |
| owner_facet | DE-91 DE-BY-TUM DE-19 DE-BY-UBM DE-20 DE-739 DE-634 DE-898 DE-BY-UBR DE-861 DE-703 DE-824 DE-83 |
| physical | 1 Online-Ressource (XXI, 434 Seiten, 68 illus., 40 illus. in color) |
| psigel | ZDB-2-SMA ZDB-2-SMA_2016 |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Birkhäuser |
| record_format | marc |
| spelling | Du, Ke-Lin Verfasser aut Search and optimization by metaheuristics techniques and algorithms inspired by nature Ke-Lin Du, M.N.S. Swamy [Basel] Birkhäuser [2016] 1 Online-Ressource (XXI, 434 Seiten, 68 illus., 40 illus. in color) txt rdacontent c rdamedia cr rdacarrier Mathematics Computer simulation Algorithms Computer mathematics Mathematical optimization Computational intelligence Computational Science and Engineering Optimization Simulation and Modeling Computational Intelligence Mathematik Swamy, M. N. S. 1935- Verfasser (DE-588)125008570 aut Erscheint auch als Druck-Ausgabe 978-3-319-41191-0 Erscheint auch als Druckausgabe 978-3-319-41191-0 https://doi.org/10.1007/978-3-319-41192-7 Verlag URL des Erstveröffentlichers Volltext HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029118869&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Du, Ke-Lin Swamy, M. N. S. 1935- Search and optimization by metaheuristics techniques and algorithms inspired by nature Mathematics Computer simulation Algorithms Computer mathematics Mathematical optimization Computational intelligence Computational Science and Engineering Optimization Simulation and Modeling Computational Intelligence Mathematik |
| title | Search and optimization by metaheuristics techniques and algorithms inspired by nature |
| title_auth | Search and optimization by metaheuristics techniques and algorithms inspired by nature |
| title_exact_search | Search and optimization by metaheuristics techniques and algorithms inspired by nature |
| title_full | Search and optimization by metaheuristics techniques and algorithms inspired by nature Ke-Lin Du, M.N.S. Swamy |
| title_fullStr | Search and optimization by metaheuristics techniques and algorithms inspired by nature Ke-Lin Du, M.N.S. Swamy |
| title_full_unstemmed | Search and optimization by metaheuristics techniques and algorithms inspired by nature Ke-Lin Du, M.N.S. Swamy |
| title_short | Search and optimization by metaheuristics |
| title_sort | search and optimization by metaheuristics techniques and algorithms inspired by nature |
| title_sub | techniques and algorithms inspired by nature |
| topic | Mathematics Computer simulation Algorithms Computer mathematics Mathematical optimization Computational intelligence Computational Science and Engineering Optimization Simulation and Modeling Computational Intelligence Mathematik |
| topic_facet | Mathematics Computer simulation Algorithms Computer mathematics Mathematical optimization Computational intelligence Computational Science and Engineering Optimization Simulation and Modeling Computational Intelligence Mathematik |
| url | https://doi.org/10.1007/978-3-319-41192-7 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029118869&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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