Swarm intelligence and bio-inspired computation: theory and applications
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| Format: | Book |
| Language: | English |
| Published: |
Amsterdam [u.a.]
Morgan Kaufmann
2013
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| Series: | Elsevier insights
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXII, 422 S. Ill. |
| ISBN: | 9780124051638 |
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| 245 | 1 | 0 | |a Swarm intelligence and bio-inspired computation |b theory and applications |c ed. by Xin-She Yang ... |
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Record in the Search Index
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|---|---|
| adam_text | Contents
List of Contributors
Preface
xv
xix
Part One Theoretical Aspects of Swarm Intelligence and
Bio-Inspired Computing
1
1
Swarm Intelligence and Bio-Inspired Computation: An Overview
3
Xin-She Yang and
Mehmet Karamanoglii
1.1
Introduction
3
1.2
Current Issues in Bio-Inspired Computing
5
1.2.1
Gaps Between Theory and Practice
5
1.2.2
Classifications and Terminology
6
1.2.3
Tuning of Algorithm-Dependent Parameters
7
1.2.4
Necessity Tor Large-Scale and Real-World Applications
7
1.2.5
Choice of Algorithms
8
1.3
Search for the Magic Formulas for Optimization
8
1.3.1
Essence of an Algorithm
8
.3.2
What Is an Ideal Algorithm?
8
.3.3
Algorithms and Self-Organization
9
.3.4
Links Between Algorithms and Self-Organization
10
.3.5
The Magic Formulas
11
1.4
Characteristics of Metaheuristics
12
.4.1
Intensification and Diversification
12
.4.2
Randomization Techniques
12
1.5
Swarm-Intelligence-Based Algorithms
13
.5.1
Ant Algorithms
13
.5.2
Bee Algorithms
14
.5.3
Bat Algorithm
14
.5.4
Particle Swarm Optimization
15
.5.5
Firefly Algorithm
16
.5.6
Cuckoo Search
17
.5.7
Flower Pollination Algorithm
19
.5.8
Other Algorithms
20
1.6
Open Problems and Further Research Topics
20
References
21
Contents
Analysis of Swarm Intelligence-Based Algorithms for Constrained
Optimization
25
M.P.
Saka,
E. Dogati
and Ibrahim Aydogdu
2.1
Introduction
25
2.2
Optimization Problems
27
2.3
Swarm Intelligence—Based Optimization Algorithms
28
2.3.1
Ant Colony Optimization
28
2.3.2
Particle Swarm Optimizer
30
2.3.3
ABC Algorithm
32
2.3.4
Glowworm Swarm Algorithm
33
2.3.5
Firefly Algorithm
35
2.3.6
Cuckoo Search Algorithm
36
2.3.7
Bat Algorithm
38
2.3.8
Hunting Search Algorithm
39
2.4
Numerical Examples
41
2.4.1
Example
1 41
2.4.2
Example
2 42
2.5
Summary and Conclusions
44
References
47
Levy Flights and Global Optimization
49
Momin Jamil and
Hans-Jürgen Zepernick
3.1
Introduction
49
3.2
Metaheuristic Algorithms
50
3.3
Levy Flights in Global Optimization
52
3.3.1
The Levy Probability Distribution
53
3.3.2
Simulation of Levy Random Numbers
54
3.3.3
Diversification and Intensification
55
3.4
Metaheuristic Algorithms Based on Levy Probability
Distribution: Is It a Good Idea?
59
3.4.1
Evolutionary Programming Using Mutations Based on the
Levy Probability Distribution
59
3.4.2
Levy Particle Swarm
60
3.4.3
Cuckoo Search
61
3.4.4
Modified Cuckoo Search
63
3.4.5
Firefly Algorithm
63
3.4.6
Eagle Strategy
66
3.5
Discussion
67
3.6
Conclusions
68
References
69
Memetic Self-Adaptive Firefly Algorithm
73
Iztok Fister, Xin-She Yang, Janez Brest and lztok Jr. Fister
4.1
Introduction
73
4.2
Optimization Problems and Their Complexity
76
Contents
4.3
Memetic Self-Adaptive Firefly Algorithm
79
4.3.1
Self-Adaptation of Control Parameters
81
4.3.2
Population Model
82
4.3.3
Balancing Between Exploration and Exploitation
83
4.3.4
The Local Search
86
4.3.5
Scheme of the MSA-FFA
87
4.4
Case Study: Graph 3-Coloring
87
4.4.1
Graph 3-Coloring
89
4.4.2
MSA-FFA for Graph 3-Coloring
90
4.4.3
Experiments and Results
92
4.5
Conclusions
98
References
99
Modeling and Simulation of Ant Colony s Labor Division:
A Problem-Oriented Approach
103
Renimi
Xiao
5.1
Introduction
103
5.2
Ant Colony s Labor Division Behavior and its Modeling Description
105
5.2.1
Ant Colony s Labor Division
105
5.2.2
Ant Colony s Labor Division Model
105
5.2.3
Some Analysis
108
5.3
Modeling and Simulation of Ant Colony s Labor Division
with Multitask
109
5.3.1
Background Analysis
109
5.3.2
Design and Implementation of Ant Colony s Labor
Division Model with Multitask
110
5.3.3
Supply Chain Virtual Enterprise Simulation
113
5.3.4
Virtual Organization Enterprise Simulation
115
5.3.5
Discussion
119
5.4
Modeling and Simulation of Ant Colony s Labor Division
with Multistate
119
5.4.1
Background Analysis
119
5.4.2
Design and Implementation of Ant Colony s Labor
Division Model with Multistate
121
5.4.3
Simulation Example of Ant Colony s Labor Division
Model with Multistate
123
5.5
Modeling and Simulation of Ant Colony s Labor Division with
Multiconstraint
127
5.5.1
Background Analysis
127
5.5.2
Design and Implementation of Ant Colony s
Labor Division Model with Multiconstraint
128
5.5.3
Simulation Results and Analysis
132
5.6
Concluding Remarks
132
Acknowledgment
134
References
134
viii Contents
6
Partide
Swarm Algorithm: Convergence and Applications
137
Shichang Sun and Hongbo Liu
6.1
Introduction
137
6.2
Convergence Analysis
139
6.2.1
Individual Trajectory
139
6.2.2
Probabilistic Analysis
142
6.3
Performance Illustration
146
6.3.1
Dataflow Application
146
6.4
Application in Hidden Markov Models
157
6.4.1
Parameters Weighted
HMM
159
6.4.2
PSO-Viterbi for Parameters Weighted HMMs
160
6.4.3
POS
Tagging Problem and Solution
160
6.4.4
Experiment
162
6.5
Conclusions
163
References
165
7
A Survey of Swarm Algorithms Applied to Discrete
Optimization Problems
169
Jonas
Krause, Jelson
Cordeiro,
Rafael Stubs Parpinelli and
Heitor
Silve rio Lopes
7.1
Introduction
169
7.2
Swarm Algorithms
170
7.2.1
Particle Swarm Optimization
170
7.2.2
Roach Infestation Optimization
171
7.2.3
Cuckoo Search Algorithm
171
7.2.4
Firefly Algorithm
171
7.2.5
Gravitational Search Algorithm
171
7.2.6
Bat Algorithm
172
7.2.7
Glowworm Swarm Optimization Algorithm
172
7.2.8
Artificial Fish School Algorithm
172
7.2.9
Bacterial Evolutionary Algorithm
173
7.2.10
Bee Algorithm
173
7.2.11
Artificial Bee Colony Algorithm
173
7.2.12
Bee Colony Optimization
174
7.2.13
Marriage in Honey-Bees Optimization Algorithm
174
7.3
Main Concerns to Handle Discrete Problems
174
7.3.1
Discretization Methods
175
7.4
Applications to Discrete Problems
177
7.4.1
Particle Swarm Optimization
177
7.4.2
Roach Infestation Optimization
178
7.4.3
Cuckoo Search Algorithm
178
7.4.4
Firefly Algorithm
178
7.4.5
Bee Algorithm
179
7.4.6
Artificial Bee Colony
179
7.4.7
Bee Colony Optimization
180
Contents ix
7.4.8
Marriage
in Honey-Bees
Optimization Algorithm
181
7.4.9
Other Swarm Intelligence Algorithms
181
7.5
Discussion
182
7.6
Concluding Remarks and Future Research
184
References
186
8
Test Functions for Global Optimization: A Comprehensive Survey
193
Momin Jamil, Xin-She Yang and
Hans-Jürgen Zepernick
8.1
Introduction
193
8.2
A Collection of Test Functions for GO
194
8.2.1
Unimodal Test Functions
196
8.2.2 Multimodal
Function
199
8.3
Conclusions
221
References
221
Part Two Applications and Case Studies
223
9
Binary Bat Algorithm for Feature Selection
225
Rodrigo
Yiiji Mizobe Nakamura, Luis
Augusto
Martins
Pereira,
Douglas
Rodrigues,
Kelton
Augusto
Pontara Costa,
João
Paulo Papa and Xin-She Yang
9.1
Introduction
225
9.2
Bat
Algorithm
226
9.3
Binary Bat Algorithm
228
9.4
Optimum-Path Forest Classifier
228
9.4.1
Background Theory
229
9.5
Binary Bat Algorithm
231
9.6
Experimental Results
233
9.7
Conclusions
236
References
237
10
Intelligent Music Composition
239
Máximos
A. Kaliakatsos-Papakostas, Andreas
Floros
and
Michael
N.
Vrahatis
10.1
Introduction
239
10.2
Unsupervised Intelligent Composition
241
10.2.1
Unsupervised Composition with Cellular Automata
241
10.2.2
Unsupervised Composition with L-Systems
243
10.3
Supervised Intelligent Composition
245
10.3.1
Supervised Composition with Genetic Algorithms
246
10.3.2
Supervised Composition Genetic Programming
247
10.4
Interactive Intelligent Composition
248
10.4.1
Composing with Swarms
250
10.4.2
Interactive Composition with GA and GP
251
10.5
Conclusions
253
References
254
x
Contents
11
A Review of the Development and Applications of the
Cuckoo Search Algorithm
257
Sean Walton. Oubay Hassan, Kenneth Morgan and M. Rowan Brown
11.1
Introduction
257
11.2
Cuckoo Search Algorithm
258
11.2.1
The Analogy
258
1
1.2.2
Cuckoo Breeding Behavior
258
11.2.3
Levy Flights
259
11.2.4
The Algorithm
259
11.2.5
Validation
261
11.3
Modifications and Developments
261
11.3.1
Algorithmic Modifications
262
11.3.2
Hybridization
264
I
1.4
Applications
265
11.4.1
Applications in Machine Learning
265
11.4.2
Applications in Design
266
11.5
Conclusion
269
References
270
12
Bio-Inspired Models for Semantic Web
273
Priti Srinivas Sajja and
Raje
mini Akerkar
12.1
Introduction
273
12.2
Semantic Web
274
12.3
Constituent Models
276
12.3.1
Artificial Neural Network
276
12.3.2
Genetic Algorithms
281
12.3.3
Swarm Intelligence
284
12.3.4
Application in Different Aspects of Semantic Web
286
12.4
Neuro-Fuzzy System for the Web Content Filtering:
Application
287
12.5
Conclusions
291
References
291
13
Discrete Firefly Algorithm for Traveling Salesman Problem:
A New Movement Scheme
295
GHang Kusuma
Jati,
Ridi
Maniirung and Suyanto
13.1
Introduction
295
13.2
Evolutionary Discrete Firefly Algorithm
297
13.2.1
The Representation of the Firefly
297
13.2.2
Light Intensity
298
13.2.3
Distance
298
13.2.4
Attractiveness
299
13.2.5
Light Absorption
299
Contents
13.2.6
Movement
300
13.2.7
Inversion Mutation
301
13.2.8
EDFA Scheme
301
13.3
A New DFA for the TSP
302
13.3.1
Edge-Based Movement
302
13.3.2
New DFA Scheme
304
13.4
Result and Discussion
305
13.4.1
Fire
Лу
Population
306
13.4.2
Effect
oľ
Light Absorption
306
13.4.3
Number of Updating Index
307
13.4.4
Performance of New DFA
309
13.5
Conclusion
311
Acknowledgment
311
References
311
14
Modeling to Generate Alternatives Using Biologically
Inspired Algorithms
313
Rciha
¡inanirad
and Julian Scott Yeomans
14.1
Introduction
313
14.2
Modeling to Generate Alternatives
314
14.3
FA for Function Optimization
316
14.4
FA-Based Concurrent Coevolutionary Computational
Algorithm for MGA
318
14.5
Computational Testing of the FA Used for MGA
321
14.6
An SO Approach for Stochastic MGA
324
14.7
Case Study of Stochastic MGA for the Expansion of
Waste Management Facilities
326
14.8
Conclusions
331
References
332
15
Structural Optimization Using
Krill
Herd Algorithm
335
Amir
Hossein Gandomi,
Amir
Hossein
Alavi
and
Siamak Talatahari
15.1
Introduction
335
15.2
Krill
Herd Algorithm
336
15.2.1
Lagrangian Model of
Krill
Herding
336
15.3
Implementation and Numerical Experiments
339
15.3.1
Case I: Structural Design of a Pin-Jointed Plane Frame
340
15.3.2
Case II: A Reinforced Concrete Beam Design
342
15.3.3
Case III: 25-Bar Space Truss Design
344
15.4
Conclusions and Future Research
346
References
348
Contents
16
Artificial
Plant
Optimization Algorithm
351
Zhihua
Cui
and Xingjuan
Cai
16.1
Introduction
351
16.2
Primary APOA
352
16.2.1
Main Method
352
16.2.2
Photosynthesis Operator
352
16.2.3
Phototropism Operator
353
16.2.4
Applications to Artificial Neural Network Training
354
16.3
Standard APOA
357
16.3.1
Drawbacks of PAPOA
357
16.3.2
Phototropism Operator
358
16.3.3
Apical Dominance Operator
359
16.3.4
Application to Toy Model of Protein Folding
360
16.4
Conclusion
363
Acknowledgment
364
References
364
17
Genetic Algorithm for the Dynamic Berth Allocation Problem
in Real Time
367
Carlos Arango, Pablo
Cortes,
Alejandro
Escudem
and Luis Onieva
17.1
Introduction
367
17.2
Literature Review
369
17.3
Optimization Model
370
17.3.1
Sets
372
17.3.2
Parameters
372
17.3.3
Decision Variables
372
17.4
Solution Procedure by Genetic Algorithm
375
17.4.1
Representation
375
17.4.2
Fitness
375
17.4.3
Selection of Parents and Genetic Operators
376
17.4.4
Mutation
376
17.4.5
Crossover
377
17.5
Results and Analysis
377
17.6
Conclusion
382
References
382
18
Opportunities and Challenges of Integrating Bio-Inspired
Optimization and Data Mining Algorithms
385
Simon Fong
18.1
Introduction
385
18.2
Challenges in Data Mining
387
18.2.1
Curse of Dimensionality
387
18.2.2
Data Streaming
389
Contents
18.3
Bio-Inspired Optimization Metaheuristics
390
18.4
The Convergence
391
18.4.1
Integrating BiCam Algorithms into Clustering
392
18.4.2
Integrating BiCam Algorithms into Feature Selection
394
18.5
Conclusion
400
References
401
19
Improvement of PSO Algorithm by Memory-Based Gradient
Search
—
Application in Inventory Management
403
Tamas Varga,
Andreis
К
і
nil
y
and Janos
Abonyi
19.1
Introduction
403
19.2
The Improved PSO Algorithm
405
19.2.1
Classical PSO Algorithm
405
19.2.2
Improved PSO Algorithm
407
19.2.3
Results
410
19.3
Stochastic Optimization ofMultiechelon Supply Chain Model
414
19.3.1
Inventory Model of a Single Warehouse
415
19.3.2
Inventory Model of a Supply Chain
417
19.3.3
Optimization Results
419
19.4
Conclusion
419
Acknowledgment
420
References
420
|
| any_adam_object | 1 |
| author2 | Yang, Xin-She 1965- |
| author2_role | edt |
| author2_variant | x s y xsy |
| author_GND | (DE-588)1043733906 |
| author_facet | Yang, Xin-She 1965- |
| building | Verbundindex |
| bvnumber | BV041086555 |
| classification_rvk | ST 300 |
| ctrlnum | (OCoLC)854727075 (DE-599)HBZHT017645101 |
| discipline | Informatik |
| format | Book |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV041086555 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:39:17Z |
| institution | BVB |
| isbn | 9780124051638 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026063237 |
| oclc_num | 854727075 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-92 |
| owner_facet | DE-473 DE-BY-UBG DE-92 |
| physical | XXII, 422 S. Ill. |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Morgan Kaufmann |
| record_format | marc |
| series2 | Elsevier insights |
| spelling | Swarm intelligence and bio-inspired computation theory and applications ed. by Xin-She Yang ... Amsterdam [u.a.] Morgan Kaufmann 2013 XXII, 422 S. Ill. txt rdacontent n rdamedia nc rdacarrier Elsevier insights Bioinformatik (DE-588)4611085-9 gnd rswk-swf Schwarmintelligenz (DE-588)4793676-9 gnd rswk-swf Verteilte künstliche Intelligenz (DE-588)4281805-9 gnd rswk-swf Swarm intelligence. Biologically-inspired computing. Algorithms. (DE-588)4143413-4 Aufsatzsammlung gnd-content Schwarmintelligenz (DE-588)4793676-9 s Verteilte künstliche Intelligenz (DE-588)4281805-9 s Bioinformatik (DE-588)4611085-9 s DE-604 Yang, Xin-She 1965- (DE-588)1043733906 edt Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026063237&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Swarm intelligence and bio-inspired computation theory and applications Bioinformatik (DE-588)4611085-9 gnd Schwarmintelligenz (DE-588)4793676-9 gnd Verteilte künstliche Intelligenz (DE-588)4281805-9 gnd |
| subject_GND | (DE-588)4611085-9 (DE-588)4793676-9 (DE-588)4281805-9 (DE-588)4143413-4 |
| title | Swarm intelligence and bio-inspired computation theory and applications |
| title_auth | Swarm intelligence and bio-inspired computation theory and applications |
| title_exact_search | Swarm intelligence and bio-inspired computation theory and applications |
| title_full | Swarm intelligence and bio-inspired computation theory and applications ed. by Xin-She Yang ... |
| title_fullStr | Swarm intelligence and bio-inspired computation theory and applications ed. by Xin-She Yang ... |
| title_full_unstemmed | Swarm intelligence and bio-inspired computation theory and applications ed. by Xin-She Yang ... |
| title_short | Swarm intelligence and bio-inspired computation |
| title_sort | swarm intelligence and bio inspired computation theory and applications |
| title_sub | theory and applications |
| topic | Bioinformatik (DE-588)4611085-9 gnd Schwarmintelligenz (DE-588)4793676-9 gnd Verteilte künstliche Intelligenz (DE-588)4281805-9 gnd |
| topic_facet | Bioinformatik Schwarmintelligenz Verteilte künstliche Intelligenz Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026063237&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT yangxinshe swarmintelligenceandbioinspiredcomputationtheoryandapplications |