Particle swarm optimisation: classical and quantum perspectives
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton, Fla.
CRC Press
2012
|
| Series: | Numerical analysis and scientifc computing
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXII, 397 S. graf. Darst. 1 CD-ROM (12 cm) |
| ISBN: | 9781439835760 |
Staff View
MARC
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| 100 | 1 | |a Sun, Jun |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Particle swarm optimisation |b classical and quantum perspectives |c Jun Sun ; Choi-Hong Lai ; Xiao-Jun Wu |
| 264 | 1 | |a Boca Raton, Fla. |b CRC Press |c 2012 | |
| 300 | |a XXII, 397 S. |b graf. Darst. |e 1 CD-ROM (12 cm) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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| 490 | 0 | |a Numerical analysis and scientifc computing | |
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| 689 | 1 | |5 DE-604 | |
| 700 | 1 | |a Lai, Choi-Hong |e Sonstige |0 (DE-588)141031263 |4 oth | |
| 700 | 1 | |a Wu, Xiao-Jun |e Sonstige |4 oth | |
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Record in the Search Index
| _version_ | 1804149509863768064 |
|---|---|
| adam_text | Contents
Preface,
xvii
Authors,
xxi
Chapter
1 ■
Introduction
1
1.1
OPTIMISATION PROBLEMS AND OPTIMISATION
METHODS
1
1.2
RANDOM SEARCH TECHNIQUES
4
1.3
METAHEURISTIC METHODS
6
1.3.1
Simulated Annealing
6
1.3.2
Evolutionary Algorithms
7
1.3.2.1
Genetic Algorithm
7
1.3.2.2
Genetic Programming
9
1.3.2.3
Evolution Strategy
9
1.3.2.4
Evolutionary Programming
10
1.3.3
Tabu Search
11
1.3.4
Differential Evolution
12
1.3.5
Swarm Intelligence Algorithms
14
1.4
SWARM INTELLIGENCE
15
1.4.1
Properties of Swarm Intelligence
15
1.4.2
Ant Colony Optimisation
15
1.4.3
Particle Swarm Optimisation
17
REFERENCES
17
viii
■ Contents
Chapter
2 ■
Particle Swarm Optimisation
__________________ 23
2.1
OVERVIEW
23
2.2
MOTIVATIONS
27
2.2.1
Collective Behaviour
27
2.2.2
Modelling and Simulation of Self-Organised
Collective Behaviour
28
2.2.3
From Simulation of Collective Behaviour
to the PSO Algorithm
30
2.3
PSO ALGORITHM: BASIC CONCEPTS
AND THE PROCEDURE
31
2.3.1
Simulation on Two-Dimensional Plane
31
2.3.2
Simulation of Multidimensional Search
34
2.3.3
Formal Description of PSO
35
2.3.4
Procedure of the PSO Algorithm
38
2.4
PARADIGM: HOW TO USE PSO TO SOLVE
OPTIMISATION PROBLEMS
39
2.4.1
Simple Benchmark Problem
39
2.4.2
Implementation of the PSO Algorithm
40
2.4.3
Results and Analysis
45
2.4.3.1
Results
45
2.4.3.2
Statistical Comparison
46
2.4.3.3
Comparison of Convergence Speed
49
2.4.3.4
Further Discussion
50
2.5
SOME HARDER EXAMPLES
52
2.5.1
Benchmark Problems
52
2.5.1.1
Introduction
52
2.5.1.2
Testing Functions
54
2.5.2
Results on Benchmark Problems
59
REFERENCES
66
Contents ■ ix
Chapter
3 ■
Some Variants of Particle Swarm Optimisation
______75
ЗЛ
WHY DOES THE PSO ALGORITHM NEED
TO BE IMPROVED?
75
3.2
INERTIA AND CONSTRICTION—ACCELERATION
TECHNIQUES FOR PSO
77
3.2.1
PSO with Inertia Weights (PSO-In)
77
3.2.1.1
Qualitative
Analysis of the Velocity
Updating Equation
77
3.2.1.2
PSO with Inertia Weight
78
3.2.1.3
Modifications of PSO Using Various
Inertia Weights
79
3.2.2
PSO with Constriction Factors (PSO-Co)
93
3.3
LOCAL BEST MODEL
94
3.3.1
Neighbourhood Topologies
95
3.3.2
PSO with a Neighbourhood Operator
95
3.3.3
Fully Informed PSO
96
3.3.4 Von
Neumann Topology
96
3.3.5
Some Dynamic Topologies
97
3.3.6
Species-Based PSO
104
3.4
PROBABILISTIC ALGORITHMS
104
3.4.1
Bare Bone PSO Family
105
3.4.2
Trimmed-Uniform PSO and Gaussian-Dynamic
PSO
107
3.4.3
Gaussian PSO and Jump Operators
109
3.4.4
Exponential PSO 111
3.4.5
Alternative Gaussian PSO
113
3.4.6
Levy PSO
114
3.4.7
PSO with Various Mutation Operators
115
3.5
OTHER VARIANTS OF PSO
124
3.5.1
Discrete PSO Algorithms
124
Contents
3.5.2
Hybrid PSO Algorithms
128
3.5.2.1
PSO with Selection Operator
128
3.5.2.2
PSO with Differential Evolution
129
3.5.2.3
PSO with Linkage Learning
129
3.5.2.4
Hybrids of PSO with GA
131
3.5.3
Other Modified PSO
133
3.5.3.1
Attractive and Repulsive PSO
133
3.5.3.2
Comprehensive Learning PSO
133
3.5.3.3
Cooperative PSO
134
3.5.3.4
Self-Organised Criticality PSO
135
3.5.3.5
Dissipative PSO
135
3.5.3.6
PSO with Spatial Particle Extension
136
REFERENCES
136
Chapter
4 ■
Quantum-Behaved Particle Swarm Optimisation
143
4.1
OVERVIEW
143
4.1.1
Introduction
143
4.1.2
Various Improvements of QPSO
144
4.1.2.1
Selecting and Controlling the Parameters
Effectively
144
4.1.2.2
Using Different Probability Distribution
145
4.1.2.3
Controlling the Diversity Measures
146
4.1.2.4
Hybridising with Other Search Techniques
146
4.1.2.5
Cooperative Mechanism
148
4.1.3
Applications of the QPSO
149
4.1.3.1
Antenna Design
149
4.1.3.2
Biomedicine
149
4.1.3.3
Mathematical Programming
149
4.1.3.4
Communication Networks
150
4.1.3.5
Control Engineering
150
4.1.3.6
Clustering and Classification
151
4.1.3.7
Electronics and Electromagnetics
152
Contents ■ xi
4.1.3.8
Finance
152
4.1.3.9
Fuzzy
Systems 152
4.1.3.10
Graphics
152
4.1.3.11
Image Processing
152
4.1.3.12
Power Systems
153
4.1.3.13
Modelling
153
4.1.3.14
Other Application Areas
154
4.2
MOTIVATION: FROM CLASSICAL DYNAMICS
TO QUANTUM MECHANICS
154
4.2.1
Dynamical Analysis of Particle Behaviour
155
4.2.1.1
Case When
(φ
-
w
-
I)2
-
4w
= 0 157
4.2.1.2
Case When
(φ
-
w
-
I)2 ~4w>
0 160
4.2.1.3
Case When
(φ
-w
-
I)2 ~4w<
0 162
4.2.2
Particle Behaviour from the Perspectives
of Quantum Mechanics
166
4.3
QUANTUM MODEL: FUNDAMENTALS OF QPSO
167
4.3.1
б
Potential Well Model
167
4.3.2
Evolution Equation of QPSO
171
4.3.3
Conditions of Convergence
172
4.3.4
Discussion
176
4.4
QPSO ALGORITHM
178
4.4.1
Two Search Strategies of QPSO
178
4.4.2
Procedure of QPSO
180
4.4.3
Properties of QPSO
180
4.4.3.1
Advantages of Using the Quantum Model
181
4.4.3.2
Knowledge Seeking of Particles in QPSO
182
4.4.3.3
Wait Effect amongst Particles
183
4.5
SOME ESSENTIAL APPLICATIONS
184
4.5.1
Applications to Unconstrained Function
Optimisation
184
4.5.2
Solutions to Constrained NPL
188
4.5.2.1
Problem Description
188
xii ■ Contents
4.5.2.2 Experiments
of
Benchmark Problems
for Constrained Optimisation
190
4.5.3
Solving Multipeak Optimisation Problems by QPSO
193
4.5.3.1
Introduction
193
4.5.3.2
SQPSO Algorithm
194
4.5.3.3
Numerical Results
196
4.5.4
Solving Systems of Non-Linear Equations
203
4.5.4.1
Objective Function of Systems
of Equations
204
4.5.4.2
Experimental Results
205
4.6
SOME VARIANTS OF QPSO
208
4.6.1
QPSO with Diversity Control Strategy
208
4.6.2
QPSO with Selection Operator
211
4.6.2.1
QPSO with Tournament Selection
212
4.6.2.2
QPSO with Roulette-Wheel Selection
212
4.6.3
QPSO with Mutation Operators
214
4.6.4
QPSO with Hybrid Distribution
215
4.6.5
Cooperative QPSO
217
4.7
SUMMARY
219
REFERENCES
220
Chapter
5 ■
Advanced Topics
229
5.1
BEHAVIOUR ANALYSIS OF INDIVIDUAL PARTICLES
229
5.1.1
Some Preliminaries
230
5.1.2
Simplification of the Iterative Update Equations
233
5.1.3
Convergence Behaviour of
Туре
-l
Particle
234
5.1.4
Boundedness of Type-2 Particle
243
5.1.5
Simulation Results
247
5.2
CONVERGENCE ANALYSIS OF THE ALGORITHM
253
5.2.1
Analysis Using Probability Measures
253
5.2.1.1
Preliminaries
253
5.2.1.2
Global Convergence of QPSO
255
Contents ■ xiii
5.2.2
Analysis Using Markov Processes
259
5.2.2.1
Preliminaries
259
5.2.2.2
Markov Process Model for a Stochastic
Optimisation Algorithm
260
5.2.2.3
Construction of a Markov Process Model
for QPSO
262
5.2.2.4
Global Convergence of a Stochastic
Optimisation Algorithm
263
5.2.2.5
Global Convergence of QPSO
265
5.2.3
Analysis in Probabilistic Metric Spaces
268
5.2.3.1
Preliminaries
268
5.2.3.2
Fixed-Point Theorem in PM Space
270
5.2.3.3
Global Convergence of the QPSO Algorithm
271
5.3
TIME COMPLEXITY AND RATE OF CONVERGENCE
277
5.3.1
Measure of Time Complexity
277
5.3.2
Convergence Rate
279
5.3.3
Testing Complexity and Convergence Rate of QPSO
282
5.4
PARAMETER SELECTION AND PERFORMANCE
COMPARISON
290
5.4.1
Methods for Parameter Control
291
5.4.2
Empirical Studies on Parameter Selection
291
5.4.3
Performance Comparison
296
5.5
SUMMARY
299
REFERENCES
300
Chapter
6 ■
Industrial Applications
_________________________303
6.1
INVERSE PROBLEMS FOR PARTIAL DIFFERENTIAL
EQUATIONS
303
6.1.1
Introduction
303
6.1.2
IHCPs
305
6.1.3
QPSO for IHCPs
307
6.1.4
Numerical Experiments and Results
309
6.1.5
Summary
313
xiv ■ Contents
6.2 INVERSE PROBLEMS
FOR NON-LINEAR
.
DYNAMICAL SYSTEMS
313
6.2.1
Introduction
313
6.2.2
Identification of Chaotic Systems by QPSO
314
6.2.3
Simulation Results
315
6.2.3.1
Brusselator
315
6.2.3.2 Lorenz
System
317
6.2.3.3
Chen System
318
6.2.4
Summary
322
6.3
OPTIMAL DESIGN OF DIGITAL FILTERS
322
6.3.1
Introduction
322
6.3.2
Problem Formulation
323
6.3.3
A 2D Filter Design Using QPSO
325
6.3.3.1
Design Example
325
6.3.3.2
Representation of the Particle and
Constraint Handling
327
6.3.4
Experimental Results and Discussions
328
6.3.4.1
Parameter Configurations
328
6.3.4.2
Simulation Results
329
6.3.5
Summary
333
6.4
ED PROBLEMS
335
6.4.1
Introduction
335
6.4.2
Problem Description
336
6.4.2.1
Formulation of the ED Problem
336
6.4.2.2
System Transmission Losses
338
6.4.2.3
Ramp Rate Limits
338
6.4.2.4
Prohibited Operating Zone
338
6.4.3
Solving ED Problems by the QPSO Algorithm
339
6.4.3.1
Representation of Individual Particle
339
6.4.3.2
Constraints Handling and Objective
Function
340
Contents ■ xv
6.4.4
Case Studies and Discussion
340
6.4.4.1
Parameter Configurations
341
6.4.4.2
Simulation Results
341
6.4.5
Summary
345
6.5
MSA
347
6.5.1
Introduction
347
6.5.2
HMM
for MSA
350
6.5.2.1
Topology of
HMM
for MSA
350
6.5.2.2
Training
HMM
for MSA
351
6.5.3
QPSO-Trained HMMs for MSA
352
6.5.3.1
Training HMMs by QPSO
352
6.5.3.2
MSA with the Trained
HMM
352
6.5.3.3
Scoring of MSA
353
6.5.4
Numerical Experiments
354
6.5.4.1
Benchmark
Datasets
354
6.5.4.2
Experimental Settings
356
6.5.4.3
Experimental Results
356
6.5.5
Summary
366
6.6
IMAGE SEGMENTATION
367
6.6.1
Introduction
367
6.6.2
OTSU Criterion-Based Measure
368
6.6.3
Performance Evaluation
370
6.6.4
Summary
376
6.7
IMAGE FUSION
376
6.7.1
Introduction
376
6.7.2
Image Segmentation Based on QPSO-FCM
376
6.7.3
Image Fusion Based on QPSO-FCM Segmentation
379
6.7.4
Experimental Results
380
6.7.5
Summary
384
REFERENCES
384
Index,
395
|
| any_adam_object | 1 |
| author | Sun, Jun |
| author_GND | (DE-588)141031263 |
| author_facet | Sun, Jun |
| author_role | aut |
| author_sort | Sun, Jun |
| author_variant | j s js |
| building | Verbundindex |
| bvnumber | BV040449551 |
| classification_rvk | ST 152 ST 300 UK 8400 |
| ctrlnum | (OCoLC)781610841 (DE-599)HBZHT016857046 |
| dewey-full | 519.60285 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.60285 |
| dewey-search | 519.60285 |
| dewey-sort | 3519.60285 |
| dewey-tens | 510 - Mathematics |
| discipline | Physik Informatik Mathematik |
| format | Book |
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| id | DE-604.BV040449551 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T00:24:10Z |
| institution | BVB |
| isbn | 9781439835760 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025297240 |
| oclc_num | 781610841 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-19 DE-BY-UBM |
| owner_facet | DE-473 DE-BY-UBG DE-19 DE-BY-UBM |
| physical | XXII, 397 S. graf. Darst. 1 CD-ROM (12 cm) |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | CRC Press |
| record_format | marc |
| series2 | Numerical analysis and scientifc computing |
| spelling | Sun, Jun Verfasser aut Particle swarm optimisation classical and quantum perspectives Jun Sun ; Choi-Hong Lai ; Xiao-Jun Wu Boca Raton, Fla. CRC Press 2012 XXII, 397 S. graf. Darst. 1 CD-ROM (12 cm) txt rdacontent n rdamedia nc rdacarrier Numerical analysis and scientifc computing Quantencomputer (DE-588)4533372-5 gnd rswk-swf Partikel-Schwarm-Optimierung (DE-588)7658941-9 gnd rswk-swf Schwarmintelligenz (DE-588)4793676-9 gnd rswk-swf Schwarmintelligenz (DE-588)4793676-9 s Partikel-Schwarm-Optimierung (DE-588)7658941-9 s DE-604 Quantencomputer (DE-588)4533372-5 s Lai, Choi-Hong Sonstige (DE-588)141031263 oth Wu, Xiao-Jun Sonstige oth Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025297240&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Sun, Jun Particle swarm optimisation classical and quantum perspectives Quantencomputer (DE-588)4533372-5 gnd Partikel-Schwarm-Optimierung (DE-588)7658941-9 gnd Schwarmintelligenz (DE-588)4793676-9 gnd |
| subject_GND | (DE-588)4533372-5 (DE-588)7658941-9 (DE-588)4793676-9 |
| title | Particle swarm optimisation classical and quantum perspectives |
| title_auth | Particle swarm optimisation classical and quantum perspectives |
| title_exact_search | Particle swarm optimisation classical and quantum perspectives |
| title_full | Particle swarm optimisation classical and quantum perspectives Jun Sun ; Choi-Hong Lai ; Xiao-Jun Wu |
| title_fullStr | Particle swarm optimisation classical and quantum perspectives Jun Sun ; Choi-Hong Lai ; Xiao-Jun Wu |
| title_full_unstemmed | Particle swarm optimisation classical and quantum perspectives Jun Sun ; Choi-Hong Lai ; Xiao-Jun Wu |
| title_short | Particle swarm optimisation |
| title_sort | particle swarm optimisation classical and quantum perspectives |
| title_sub | classical and quantum perspectives |
| topic | Quantencomputer (DE-588)4533372-5 gnd Partikel-Schwarm-Optimierung (DE-588)7658941-9 gnd Schwarmintelligenz (DE-588)4793676-9 gnd |
| topic_facet | Quantencomputer Partikel-Schwarm-Optimierung Schwarmintelligenz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025297240&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT sunjun particleswarmoptimisationclassicalandquantumperspectives AT laichoihong particleswarmoptimisationclassicalandquantumperspectives AT wuxiaojun particleswarmoptimisationclassicalandquantumperspectives |