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Particle Swarm Optimization [Kõva köide]

  • Formaat: Hardback, 244 pages, kõrgus x laius x paksus: 242x163x19 mm, kaal: 506 g
  • Ilmumisaeg: 07-Feb-2006
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1905209045
  • ISBN-13: 9781905209040
Teised raamatud teemal:
Particle Swarm OptimizationSuurem pilt
  • Formaat: Hardback, 244 pages, kõrgus x laius x paksus: 242x163x19 mm, kaal: 506 g
  • Ilmumisaeg: 07-Feb-2006
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1905209045
  • ISBN-13: 9781905209040
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781905209040.html
Particles, information link, memory, and cooperation are discussed in this introduction to particle swarm optimization. Starting with a simple but efficient parametric version, this manual shows how to adapt the basic principles for an enhanced, fully adaptive version. All source programs are either included or available for free download.


This is the first book devoted entirely to Particle Swarm Optimization (PSO), which is a non-specific algorithm, similar to evolutionary algorithms, such as taboo search and ant colonies.

Since its original development in 1995, PSO has mainly been applied to continuous-discrete heterogeneous strongly non-linear numerical optimization and it is thus used almost everywhere in the world. Its convergence rate also makes it a preferred tool in dynamic optimization.

Foreword 13(4)
Introduction 17(4)
Part I. Particle Swarm Optimization
21(172)
What is a Difficult Problem?
23(6)
An intrinsic definition
23(2)
Estimation and practical measurement
25(1)
For ``amatheurs'': some estimates of difficulty
26(2)
Function DΣd=1 Xd
27(1)
Function DΣd=1 X2d
27(1)
Function DΣd=1 √Xd| sin (Xd)|
27(1)
Traveling salesman on D cities
28(1)
Summary
28(1)
On a Table Corner
29(8)
Apiarian metaphor
29(1)
An aside on the spreading of a rumor
30(1)
Abstract formulation
30(4)
What is really transmitted
34(1)
Cooperation versus competition
35(1)
For ``amatheurs'': a simple calculation of propagation of rumor
35(1)
Summary
36(1)
First Formulations
37(14)
Minimal version
37(7)
Swarm size
37(1)
Information links
38(1)
Initialization
38(1)
Equations of motion
39(1)
Interval confinement
40(2)
Proximity distributions
42(2)
Two common errors
44(1)
Principal drawbacks of this formulation
45(3)
Distribution bias
45(3)
Explosion and maximum velocity
48(1)
Manual parameter setting
48(1)
For ``amatheurs'': average number of informants
49(1)
Summary
50(1)
Benchmark Set
51(8)
What is the purpose of test functions?
51(1)
Six reference functions
52(1)
Representations and comments
52(4)
For ``amatheurs'': estimates of levels of difficulty
56(2)
Theoretical difficulty
56(1)
Tripod
56(1)
Alpine 10D
57(1)
Rosenbrock
57(1)
Difficulty according to the search effort
58(1)
Summary
58(1)
Mistrusting Chance
59(12)
Analysis of an anomaly
59(2)
Computing randomness
61(1)
Reproducibility
61(1)
On numerical precision
62(1)
The rare Kiss
62(2)
Brief description
63(1)
Test of Kiss
64(1)
On the comparison of results
64(1)
For ``amatheurs'': confidence in the estimate of a rate of failure
65(3)
C programs
68(1)
Summary
69(2)
First Results
71(16)
A simple program
71(1)
Overall results
72(1)
Robustness and performance maps
73(7)
Theoretical difficulty and noted difficulty
80(1)
Source code of OEP 0
80(5)
Summary
85(2)
Swarm: Memory and Graphs of Influence
87(16)
Circular neighborhood of the historical PSO
87(1)
Memory-swarm
88(2)
Fixed topologies
90(2)
Random variable topologies
92(1)
Direct recruitment
92(1)
Recruitment by common channel of communication
92(1)
Influence of the number of informants
93(2)
In fixed topology
93(2)
In random variable topology
95(1)
Influence of the number of memories
95(2)
Reorganizations of the memory-swarm
97(2)
Mixing of the memories
97(1)
Queen and other centroids
98(1)
Comparative results
98(1)
For ``amatheurs'': temporal connectivity in random recruitment
99(2)
Summary
101(2)
Distributions of Proximity
103(18)
The random possibilities
103(1)
Review of rectangular distribution
104(1)
Alternative distributions of possibilities
105(8)
Ellipsoidal positive sectors
105(1)
Independent Gaussians
106(1)
Local by independent Gaussians
107(1)
The class of one-dimensional distributions
107(1)
Pivots
108(4)
Adjusted ellipsoids
112(1)
Some comparisons of results
113(3)
For ``amatheurs''
116(2)
Squaring of a hypersphere
116(1)
From sphere to ellipsoid
117(1)
Random volume for an adjusted ellipsoid
117(1)
Uniform distribution in a D-sphere
118(1)
C program of isotropic distribution
118(1)
Summary
119(2)
Optimal Parameter Settings
121(8)
Defense of manual parameter setting
121(1)
Better parameter settings for the benchmark set
122(5)
Search space
122(1)
To optimize the optimizer
123(2)
Analysis of results
125(1)
Rate of failure
125(1)
Distribution
125(1)
Topology and the number of informants
125(1)
Informants K
125(1)
Coefficient φ
126(1)
Informants N and memories M
126(1)
Towards adaptation
127(1)
For ``amatheurs'': number of graphs of information
127(1)
Summary
128(1)
Adaptations
129(10)
Demanding criteria
129(1)
Criterion 1
129(1)
Criterion 2
129(1)
Rough sketches
130(5)
Weighting with temporal decrease
130(1)
Selection and replacement
131(1)
Parametric adaptations
132(1)
Nonparametric adaptations
133(2)
For ``amatheurs''
135(3)
Formulas of temporal decrease
135(1)
Parametric adaptations
136(1)
Case 1 (mi ≥ 0)
137(1)
Case 2 (mi < 0)
137(1)
Summary
138(1)
Tribes or Cooperatin of Tribes
139(12)
Towards an ultimate program
139(2)
Description of Tribes
141(6)
Tribes
141(1)
The tribal relationships
141(1)
Quality of a particle
141(1)
Quality of a tribe
142(1)
Evolution of the tribes
142(1)
Removal of a particle
142(2)
Generation of a particle
144(1)
Strategies of displacement
145(1)
Best informant
146(1)
Direct comparison, general case
147(1)
Comparison by pseudo-gradients, metric spaces
147(1)
Results on the benchmark set
147(2)
Summary
149(2)
On the Constraints
151(16)
Some preliminary reflections
151(1)
Representation of the constraints
152(1)
Imperative constraints and indicative constraints
153(1)
Interval confinement
154(1)
Discrete variable
154(2)
Direct method
155(1)
List not ordered (and not orderable)
155(1)
Ordered list
155(1)
Indirect method
155(1)
Granularity confinement
156(1)
``all different'' confinement
156(1)
Confinement by dichotomy
157(1)
Multicriterion treatment
158(3)
Treatment by penalties
161(1)
C source code. Dichotomic search in a list
162(1)
For ``amatheurs''
162(3)
Summary
165(2)
Problems and Application
167(22)
Ecological niche
167(1)
Typology and choice of problems
168(1)
Canonical representation of a problem of optimization
169(1)
Knapsack
169(1)
Magic squares
170(1)
Quadratic assignment
171(1)
Traveling salesman
172(1)
Hybrid JM
173(1)
Training of a neural network
174(3)
Exclusive OR
175(1)
Diabetes among Pima Indians
176(1)
Servomechanism
176(1)
Comparisons
176(1)
Pressure vessel
177(5)
Continuous relaxed form
179(1)
Complete discrete form
180(2)
Compression spring
182(3)
Moving Peaks
185(3)
For ``amatheurs'': the magic of squares
188(1)
Summary
188(1)
Conclusion
189(4)
End of the beginning
189(1)
Mono, poly, meta
189(1)
The beginning of the end?
190(3)
Part II. Outlines
193(38)
On Parallelism
195(6)
The short-sighted swarm
195(1)
A parallel model
195(1)
A counter-intuitive result
196(1)
Qualitative explanation
197(1)
For ``amatheurs'': probability of questioning an improved memory
198(1)
Summary
199(2)
Combinatorial Problems
201(10)
Difficulty of chaos
201(1)
Like a crystal
202(1)
Confinement method
203(1)
Canonical PSO
204(6)
Summary
210(1)
Dynamics of a Swarm
211(14)
Motivations and tools
211(1)
An example with the magnifying glass
212(5)
One particle
212(2)
Two particles
214(3)
Energies
217(3)
Definitions
217(1)
Evolutions
218(2)
For experienced ``amatheurs'': convergence and constriction
220(4)
Criterion of convergence
220(1)
Coefficients of constriction
221(1)
Positive discriminant
222(2)
Summary
224(1)
Techniques and Alternatives
225(6)
Reprise
225(1)
Stop-restart/reset
226(1)
A criterion of abandonment
226(1)
Guided re-initialization
227(1)
Multi-swarm
227(1)
Dynamic optimization
228(1)
For ``amatheurs''
229(1)
Maximum flight and criterion of abandonment
229(1)
Dilation
230(1)
Summary
230(1)
Further Information 231(2)
Bibliography 233(6)
Index 239


Maurice Clerc is recognized as one of the foremost PSO specialists in the world. A former France Telecom Research and Development engineer, he maintains his research activities as a consultant for the XPS (eXtended Particle Swarm) project.