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E-raamat: Nature-Inspired Computing: Physics and Chemistry-Based Algorithms

(University of Ulster, Magee, UK), (The Ohio State University, Columbus, USA)
  • Formaat: 622 pages
  • Ilmumisaeg: 19-May-2017
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781482244830
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Nature-Inspired Computing: Physics and Chemistry-Based AlgorithmsSuurem pilt
  • Formaat: 622 pages
  • Ilmumisaeg: 19-May-2017
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781482244830
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Nature-Inspired Computing: Physics and Chemistry-Based Algorithms provides a comprehensive introduction to the methodologies and algorithms in nature-inspired computing, with an emphasis on applications to real-life engineering problems.

The research interest for Nature-inspired Computing has grown considerably exploring different phenomena observed in nature and basic principles of physics, chemistry, and biology. The discipline has reached a mature stage and the field has been well-established. This endeavour is another attempt at investigation into various computational schemes inspired from nature, which are presented in this book with the development of a suitable framework and industrial applications.

Designed for senior undergraduates, postgraduates, research students, and professionals, the book is written at a comprehensible level for students who have some basic knowledge of calculus and differential equations, and some exposure to optimization theory. Due to the focus on search and optimization, the book is also appropriate for electrical, control, civil, industrial and manufacturing engineering, business, and economics students, as well as those in computer and information sciences.

With the mathematical and programming references and applications in each chapter, the book is self-contained, and can also serve as a reference for researchers and scientists in the fields of system science, natural computing, and optimization.
Foreword xvii
Preface xix
Acknowledgments xxiii
Authors xxv
Chapter 1 Dialectics of Nature: Inspiration for Computing 1(50)
1.1 Inspiration from Nature
1(1)
1.2 Brief History of Natural Sciences
2(12)
1.2.1 Laws of Motion
3(1)
1.2.2 Law of Gravitation
3(1)
1.2.3 Transformation between Heat and Mechanical Energy
4(3)
1.2.4 Transformation between Mass and Energy
7(2)
1.2.5 Light and Optics
9(1)
1.2.6 Sound and Acoustics
9(1)
1.2.7 Hydrology and Dynamics
10(1)
1.2.8 Development in Chemistry
11(1)
1.2.9 Development in Biological Sciences
12(2)
1.3 Traditional Approaches to Search and Optimization
14(13)
1.3.1 Line Search
16(1)
1.3.2 Golden Section Search
16(1)
1.3.3 Fibonacci Search
17(1)
1.3.4 Newton's Method
17(1)
1.3.5 Secant Method
17(1)
1.3.6 Gradient-Based Methods
18(1)
1.3.6.1 Descent Methods
18(1)
1.3.6.2 Gradient Methods
19(1)
1.3.6.3 Steepest Descent Method (or Gradient Descent)
19(1)
1.3.7 Classical Newton's Method
19(1)
1.3.8 Modified Newton's Method
20(1)
1.3.9 Levenberg-Marquardt Modification
20(1)
1.3.10 Quasi-Newton Method
21(1)
1.3.11 Conjugate Direction Methods
21(1)
1.3.12 Conjugate Gradient Methods
22(1)
1.3.13 BFGS Method
23(1)
1.3.14 Deterministic vs Stochastic Algorithms
23(2)
1.3.15 Local Search Methods
25(27)
1.3.15.1 Scatter Search
26(1)
1.3.15.2 Tabu Search (TS)
26(1)
1.3.15.3 Random Search (RS)
26(1)
1.3.15.4 Downhill Simplex (Nelder-Mead) Method
26(1)
1.4 Paradigm of NIC
27(1)
1.5 Physics-Based Algorithms
28(3)
1.6 Chemistry-Based Algorithms
31(1)
1.7 Biology-Based Algorithms
32(4)
1.8 Culture-, Society-, and Civilization-Based Algorithms
36(1)
1.9 Overview
37(3)
1.10 Conclusion
40(1)
References
40(11)
Chapter 2 Gravitational Search Algorithm 51(68)
2.1 Introduction
51(1)
2.2 Physics of Gravity
52(2)
2.2.1 Acceleration of Objects
53(1)
2.2.2 Mass of Moving Objects
53(1)
2.3 Gravitational Search Algorithm
54(7)
2.4 Parameters of GSA
61(1)
2.5 Fitness Function
62(1)
2.5.1 Fitness Scaling
62(1)
2.6 Variants of GSA
63(18)
2.6.1 Binary GSA
63(2)
2.6.2 Chaotic GSA
65(1)
2.6.3 Piece-Wise Linear Chaotic Map and Sequential Quadratic Programming with GSA
66(1)
2.6.4 GSA with Chaotic Local Search
67(1)
2.6.5 Discrete GSA
68(1)
2.6.6 Mass-Dispersed GSA
69(1)
2.6.7 Opposition-Based GSA
70(1)
2.6.8 GSA with Wavelet Mutation
71(1)
2.6.9 Quantum-Inspired GSA
72(1)
2.6.10 Quantum-Inspired BGSA
73(1)
2.6.11 Piece-Wise Function-Based GSA
74(1)
2.6.12 Adaptive GSA
75(1)
2.6.13 Mutation-Based GSA
76(2)
2.6.13.1 Sign Mutation
76(1)
2.6.13.2 Reordering Mutation
77(1)
2.6.14 Disruption-Based GSA
78(1)
2.6.15 Random Local Extrema-Based GSA
79(1)
2.6.16 Modified GSA
80(1)
2.7 Hybrid GSA
81(6)
2.7.1 Hybrid PSO-GSA
81(3)
2.7.2 Hybrid GSA-EA
84(1)
2.7.3 Hybrid Fuzzy GSA
85(1)
2.7.4 Hybrid QBGSA-K-NN
86(1)
2.7.5 Hybrid K-Harmonic Means and GSA
86(1)
2.8 Application to Engineering Problems
87(23)
2.8.1 Benchmark Function Optimization
87(1)
2.8.2 Combinatorial Optimization Problems
88(1)
2.8.3 Economic Load Dispatch (ELD) Problem
88(1)
2.8.4 Economic and Emission Dispatch (EED) Problem
89(2)
2.8.5 Optimal Power Flow (OPF) Problem
91(1)
2.8.6 Reactive Power Dispatch (RPD) Problem
92(3)
2.8.7 Energy Management System (EMS)
95(1)
2.8.8 Clustering Problem
96(2)
2.8.9 Classification Problem
98(1)
2.8.10 Feature Subset Selection (FSS)
99(2)
2.8.11 Parameter Identification
101(3)
2.8.12 Training NNs
104(1)
2.8.13 Traveling Salesman Problem (TSP)
105(1)
2.8.14 Filter Design and Communication Systems
106(1)
2.8.15 Unit Commitment Problem (UCP)
107(2)
2.8.16 Multiobjective Optimization Problem (MOOP)
109(1)
2.8.17 Fuzzy Controller Design
109(1)
2.9 Conclusion
110(1)
References
110(9)
Chapter 3 Central Force Optimization 119(40)
3.1 Introduction
119(1)
3.2 Central Force Optimization Metaphor
119(5)
3.3 CFO Algorithm
124(4)
3.4 Parameters of the CFO Algorithm
128(1)
3.5 Decision Space and Probe Distribution
129(3)
3.5.1 Standard or Fully Connected
129(1)
3.5.2 Linear
130(1)
3.5.3 Mesh
130(1)
3.5.4 Ring
130(1)
3.5.5 Star
130(1)
3.5.6 Static Tree
131(1)
3.5.7 Toroidal
131(1)
3.6 Variants of CFO
132(8)
3.6.1 Simple CFO
132(1)
3.6.2 Extended CFO
133(1)
3.6.3 Pseudo-Random CFO
134(1)
3.6.4 Parameter-Free CFO
135(1)
3.6.5 Improved CFO
136(2)
3.6.6 CFO with Acceleration Clipping
138(1)
3.6.7 Binary CFO
138(2)
3.6.8 Multistart CFO
140(1)
3.7 Hybrid CFO
140(3)
3.7.1 Hybrid CFO-Nelder-Mead (CFO-NM)
140(1)
3.7.2 Hybrid CFO and Intelligent State Space Pruning
141(1)
3.7.3 Multistart or Modified CFO (MCFO)
142(1)
3.7.4 Hybrid CFO and Hill-Climbing
143(1)
3.8 Applications to Engineering Problems
143(11)
3.8.1 Electronic Circuit Design
143(1)
3.8.2 Antenna Design
144(3)
3.8.3 Benchmark Function Optimization
147(3)
3.8.4 Training Neural Network
150(1)
3.8.5 Water Pipe Networks
151(2)
3.8.6 Multiobjective CFO Algorithm
153(1)
3.9 Convergence of CFO
154(1)
3.10 Conclusion
155(1)
References
155(4)
Chapter 4 Electromagnetism-Like Optimization 159(36)
4.1 Introduction
159(1)
4.2 EMO Algorithm
160(2)
4.3 Variants of EMO
162(21)
4.3.1 EMO Variants Based on Parameters
163(6)
4.3.1.1 Revised EMO
163(1)
4.3.1.2 Discrete EMO (DEMO)
164(2)
4.3.1.3 Opposition-Based EMO
166(1)
4.3.1.4 Improved EMO
167(1)
4.3.1.5 Multipopulation EMO
167(2)
4.3.1.6 Memory-Based EMO
169(1)
4.3.2 Hybrid EMO with Other Meta-Heuristics
169(14)
4.3.2.1 Hybrid Modified EMO and Scatter Search
169(1)
4.3.2.2 Hybrid EMO and Restarted Arnoldi Algorithm
170(1)
4.3.2.3 Hybrid EMO and Iterated Swap Procedure
171(1)
4.3.2.4 Hybrid EMO and SA
172(1)
4.3.2.5 Hybrid EMO and Solis-Wets Search
173(1)
4.3.2.6 Hybrid EMO and Great Deluge (GD)
174(2)
4.3.2.7 Hybrid EMO and GA
176(1)
4.3.2.8 Species-Based Improved EMO
177(1)
4.3.2.9 Hybrid EMO and Davidon-Fletcher-Powell Search
178(1)
4.3.2.10 Hybrid EMO and PSO
179(1)
4.3.2.11 Hybrid EMO and TS
180(1)
4.3.2.12 Hybrid EMO and DE
181(2)
4.3.2.13 Opposite Sign Test-Based EMO (EMO-OST)
183(1)
4.4 Applications to Engineering Problems
183(7)
4.4.1 Constrained Optimization Problem
183(1)
4.4.2 Traveling Salesman Problem
184(1)
4.4.3 Timetabling Problem
184(1)
4.4.4 Job Shop Scheduling Problem
184(1)
4.4.5 Knapsack Problem (KP)
185(1)
4.4.6 Set Covering Problem
185(1)
4.4.7 Feature Subset Selection
185(1)
4.4.8 Inverse Kinematics Problem in Robotics
186(1)
4.4.9 Vehicle Routing Problem
186(1)
4.4.10 Maximum Betweenness Problem
186(1)
4.4.11 Redundancy Allocation Problem (RAP)
186(1)
4.4.12 Uncapacitated Multiple Allocation p-hub Median Problem
187(1)
4.4.13 Resource-Constrained Project Scheduling (RCPS) Problem
188(1)
4.4.14 Multiobjective Optimization Problem
189(1)
4.4.15 Other Applications
189(1)
4.5 Conclusions
190(1)
References
190(5)
Chapter 5 Harmony Search 195(96)
5.1 Introduction
195(1)
5.2 Harmony in Music
196(1)
5.3 Musical Improvisation
197(3)
5.4 Harmony Memory
200(2)
5.5 Harmony Search Algorithm
202(4)
5.5.1 Initializing the Optimization Problem and Algorithm Parameters
203(1)
5.5.2 Initializing HM
203(1)
5.5.3 Improvising Harmony from HM
203(3)
5.5.3.1 Harmony Memory Consideration
204(1)
5.5.3.2 Random Consideration
205(1)
5.5.3.3 Pitch Adjustment
205(1)
5.5.4 Updating HM
206(1)
5.5.5 Stopping Condition
206(1)
5.6 Characteristic Features of Parameters in the HSA
206(2)
5.7 Variants of the HSA
208(47)
5.7.1 HSA Variants Based on Parameters
208(28)
5.7.1.1 Binary Harmony Search (HS)
209(1)
5.7.1.2 Improved HS (IHS)
210(3)
5.7.1.3 Global Best HS (GHS)
213(2)
5.7.1.4 Adaptive HS
215(2)
5.7.1.5 Self-Adaptive GHS
217(1)
5.7.1.6 Discrete HS
218(1)
5.7.1.7 Chaotic HS (CHS)
219(1)
5.7.1.8 Gaussian HS
220(2)
5.7.1.9 Innovative GHS
222(1)
5.7.1.10 Dynamic/Parameter Adaptive HS
223(1)
5.7.1.11 Explorative HS (EHS)
224(1)
5.7.1.12 Quantum-Inspired HS
225(3)
5.7.1.13 Opposition-Based HS
228(1)
5.7.1.14 Cellular HS
229(2)
5.7.1.15 Design-Driven HS (DDHS)
231(1)
5.7.1.16 Island-Based HS
232(1)
5.7.1.17 Harmony Memory (HM) Initialization
233(1)
5.7.1.18 Grouping HS
234(1)
5.7.1.19 Multiple Pitch Adjustment Rate HS (PAR HS)
235(1)
5.7.1.20 Geometric Selective HS
235(1)
5.7.1.21 Adaptive Binary HS
235(1)
5.7.2 HSA Variants Based on Hybridization with Other Methods
236(19)
5.7.2.1 Hybridizing HS with Other Meta-Heuristic Algorithms
236(18)
5.7.2.2 Hybridizing HS Components into Other Meta-Heuristic Algorithms
254(1)
5.8 Application of HSA to Engineering Problems
255(21)
5.8.1 Function and Constrained Optimization Problems
255(1)
5.8.2 Structural Design Optimization
256(2)
5.8.3 Hydrologic Model Optimization
258(1)
5.8.4 Water Distribution Network (WDN)
259(1)
5.8.5 Water Pump Switching Problem
260(2)
5.8.6 Transmission Network Expansion Planning Problem
262(2)
5.8.7 Job Shop Scheduling
264(1)
5.8.8 Timetabling and Rostering Problem
265(1)
5.8.9 Training NN
266(2)
5.8.10 Clustering Problem
268(3)
5.8.11 Combined Heat and Power Economic Dispatch Problem
271(2)
5.8.12 ELD Problem
273(1)
5.8.13 Economic and Emission Dispatch Problem
274(1)
5.8.14 MOOP
275(1)
5.9 Conclusion
276(1)
References
277(14)
Chapter 6 Water Drop Algorithm 291(38)
6.1 Introduction
291(1)
6.2 River Systems
291(4)
6.2.1 Sediment Production, Transport, and Storage in the Working River
292(3)
6.3 Natural WDs
295(4)
6.4 WDs Algorithm
299(3)
6.5 Parameters of WDA
302(2)
6.6 Convergence Analysis
304(2)
6.7 Variants of WDA
306(3)
6.7.1 Improved WDA
306(1)
6.7.2 Modified WDA
307(1)
6.7.3 Adaptive WDA
308(1)
6.7.4 WDA Continuous Optimization Algorithm
308(1)
6.8 Applications to Engineering Problems
309(17)
6.8.1 Traveling Salesman Problem
310(1)
6.8.2 The n-Queen Problem
311(2)
6.8.3 Multidimensional Knapsack Problem
313(1)
6.8.4 Vehicle Routing Problem (VRP)
313(1)
6.8.5 Economic Load Dispatch Problem
314(1)
6.8.6 Combined Economic and Emission Dispatch Problem
315(1)
6.8.7 Reactive Power Dispatch Problem
316(1)
6.8.8 Vehicle Guidance in Road Graph Networks
317(1)
6.8.9 Path Planning
317(1)
6.8.10 Trajectory Planning
318(1)
6.8.11 FS
319(1)
6.8.12 Automatic Multilevel Thresholding
319(1)
6.8.13 Data Aggregation and Routing in Wireless Networks
320(2)
6.8.14 Mobile ad hoc Networks (MANET)
322(1)
6.8.15 JSSP
322(1)
6.8.16 Web Service Selection
322(1)
6.8.17 Max-Clique Problem
323(1)
6.8.18 Reservoir Operation Problem
323(1)
6.8.19 Data Clustering
324(1)
6.8.20 Steiner Tree Problem
325(1)
6.8.21 Other Applications
325(1)
6.9 Conclusion
326(1)
References
326(3)
Chapter 7 Spiral Dynamics Algorithms 329(34)
7.1 Introduction
329(1)
7.2 Spiral Phenomena in Nature
329(1)
7.3 Parametric Representation of Curves
329(8)
7.3.1 Parametric Representation of Spirals
330(9)
7.3.1.1 Archimedes or Arithmetic Spiral
332(1)
7.3.1.2 Hyperbolic Spirals
333(1)
7.3.1.3 Farmat's or Parabolic Spiral
334(1)
7.3.1.4 Lituus Spiral
335(1)
7.3.1.5 Clothoid Spirals
336(1)
7.4 Logarithmic Spirals
337(2)
7.5 Spiral Models
339(5)
7.5.1 2-D Spiral Models
339(2)
7.5.2 n-Dimensional Spiral Models
341(3)
7.6 SpD-Based Optimization Algorithm
344(2)
7.6.1 2-D Spiral Dynamics Optimization Algorithm
344(1)
7.6.2 n-Dimensional SpDO Algorithm
345(1)
7.6.3 Parameters of SpDO Algorithm
345(1)
7.7 Variants of SpDO Algorithm
346(8)
7.7.1 Adaptive SpDO Algorithm
346(3)
7.7.1.1 Linear Adaptive SpDO
347(1)
7.7.1.2 Quadratic Adaptive SpDO
348(1)
7.7.1.3 Exponential Adaptive SpDO
348(1)
7.7.1.4 Fuzzy Adaptive SpDO
348(1)
7.7.2 Hybrid SpDO Algorithms
349(6)
7.7.2.1 Hybrid Spiral-Dynamics Bacterial-Chemotaxis Algorithm
349(1)
7.7.2.2 Hybrid Spiral-Dynamics Random-Chemotaxis Algorithm
350(1)
7.7.2.3 Hybrid Spiral-Dynamics Bacterial-Foraging Algorithm
351(3)
7.8 Stability of Spiral Models
354(1)
7.9 Applications to Engineering Problems
355(5)
7.9.1 Modeling and Control Design
357(1)
7.9.2 Training of Neural Network
358(1)
7.9.3 Combined Economic and Emission Dispatch
359(1)
7.9.4 Clustering Applications
359(1)
7.9.5 Heat Sink Design
360(1)
7.10 Conclusions
360(1)
References
360(3)
Chapter 8 Simulated Annealing 363(52)
8.1 Introduction
363(1)
8.2 Principles of Statistical Thermodynamics
363(1)
8.3 Annealing Process
364(1)
8.4 SA Algorithm
365(2)
8.5 Cooling (Annealing) Schedule
367(9)
8.5.1 Monotonic Schedules (or Simple Time Schedule)
369(3)
8.5.2 Geometric (or Exponential) Cooling Schedule
372(1)
8.5.3 Adaptive Cooling
372(2)
8.5.4 Initial Temperature
374(1)
8.5.5 Final Temperature
375(1)
8.5.6 Stopping Condition
375(1)
8.6 Neighborhoods
376(1)
8.7 Variants of SA
377(7)
8.7.1 Boltzmann Annealing
377(1)
8.7.2 Fast Annealing
377(1)
8.7.3 Very Fast Simulated Reannealing
378(1)
8.7.4 Adaptive SA
378(1)
8.7.5 Discrete SA
379(1)
8.7.6 Coupled SA
379(2)
8.7.7 Modified SA
381(1)
8.7.8 Corana SA
381(1)
8.7.9 Orthogonal SA
382(1)
8.7.10 Chaotic SA
383(1)
8.7.11 Quantum Annealing
384(1)
8.8 Hybrid SA
384(9)
8.8.1 Hybrid SA and GA
384(1)
8.8.2 Hybrid Harmony Search-Based SA
385(2)
8.8.3 Hybrid PSO-SA
387(2)
8.8.4 Hybrid Ant Colony Optimization and SA
389(1)
8.8.5 Hybrid ACO, GA, and SA
390(1)
8.8.6 Hybrid DE and SA
390(1)
8.8.7 Hybrid Artificial Immune System and SA
391(1)
8.8.8 Noising Method with SA
392(1)
8.9 Convergence Analysis
393(1)
8.10 Application to Engineering Problems
394(12)
8.10.1 Travelling Salesman Problem
395(1)
8.10.2 Job-Shop Scheduling Problem
396(3)
8.10.3 Training NN
399(1)
8.10.4 Clustering Problem
400(3)
8.10.5 Vertex Covering Problem
403(2)
8.10.6 Flow Shop Sequencing Problem
405(1)
8.10.7 Multiobjective Optimization
406(1)
8.11 Conclusion
406(1)
References
407(8)
Chapter 9 Chemical Reaction Optimization 415(32)
9.1 Introduction
415(5)
9.2 Mechanisms of Chemical Reaction
420(1)
9.3 Chemical Reaction Optimization
420(5)
9.4 Features of CRO
425(2)
9.5 Parameters of CRO
427(1)
9.5.1 CRO Operators
427(1)
9.6 Variants of CRO
428(5)
9.6.1 Real-Coded CRO (RCCRO)
428(2)
9.6.2 Opposition-Based CRO
430(1)
9.6.3 Orthogonal CRO
431(1)
9.6.4 Adaptive Collision CRO
431(1)
9.6.5 Elitist CRO
432(1)
9.6.6 Artificial Chemical Reaction Optimization (ACRD) Algorithm
432(1)
9.7 Hybrid CRO
433(2)
9.7.1 Hybrid CRO and DE
433(1)
9.7.2 Hybrid CRO and PSO
433(1)
9.7.3 Hybrid CRO and Lin-Kernighan Local Search
434(1)
9.8 Application of CRO
435(8)
9.8.1 Quadratic Assignment Problem
435(2)
9.8.2 Traveling Salesman Problem
437(1)
9.8.3 Resource-Constrained Project Scheduling Problem
437(1)
9.8.4 Economic Load Dispatch Problem
438(1)
9.8.5 Optimal Power Flow Problem
439(1)
9.8.6 Training Neural Networks
439(1)
9.8.7 Fuzzy Rules Learning
439(1)
9.8.8 Communications and Networking Problems
440(2)
9.8.8.1 Peer-to-Peer Streaming
440(1)
9.8.8.2 Cognitive Radio Spectrum Allocation Problem
440(1)
9.8.8.3 Channel Assignment Problem
440(1)
9.8.8.4 Network Coding Optimization Problem
441(1)
9.8.8.5 Bus Sensor Deployment Problems
441(1)
9.8.9 Multiobjective Optimization Problems
442(1)
9.8.10 Other Applications
442(1)
9.9 Conclusion
443(1)
References
443(4)
Chapter 10 Miscellaneous Algorithms 447(106)
10.1 Introduction
447(1)
10.2 Big Bang-Big Crunch (BB-BC) Algorithm
447(4)
10.2.1 Inspiration and Algorithm
447(3)
10.2.2 Applications
450(1)
10.3 Black Hole Algorithm
451(4)
10.3.1 Inspiration and Algorithm
451(4)
10.3.2 Applications
455(1)
10.4 Galaxy-Based Search
455(4)
10.4.1 Inspiration and Algorithm
455(4)
10.4.1.1 Spiral Chaotic Move
456(1)
10.4.1.2 Local Search
457(2)
10.4.2 Applications
459(1)
10.5 Artificial Physics Optimization
459(10)
10.5.1 Inspiration and Algorithm
459(4)
10.5.2 Vector Model APO
463(1)
10.5.3 Hybrid Vector APO
464(1)
10.5.4 Extended APO Algorithm
465(1)
10.5.5 Local APO Algorithm
466(1)
10.5.6 APO with Feasibility-Based Rule
467(1)
10.5.7 Applications
468(1)
10.6 Space Gravitational Optimization
469(6)
10.6.1 Inspiration and Algorithm
469(3)
10.6.2 Modified SGO
472(1)
10.6.3 Consideration of Shape of Universe in SGO
473(1)
10.6.4 Applications
474(1)
10.7 Integrated Radiation Optimization
475(4)
10.7.1 Inspiration and Algorithm
475(3)
10.7.2 Applications
478(1)
10.8 Gravitational Interactions Optimization
479(2)
10.8.1 Inspiration and Algorithm
479(2)
10.8.2 Applications
481(1)
10.9 Charged System Search
481(14)
10.9.1 Inspiration and Algorithm
481(5)
10.9.2 Discrete CSS
486(1)
10.9.3 Chaotic CSS
486(2)
10.9.4 Adaptive CSS
488(1)
10.9.5 Magnetic CSS
489(3)
10.9.6 Hybrid CSS
492(1)
10.9.7 Applications
493(2)
10.10 Hysteretic Optimization
495(2)
10.10.1 Inspiration and Algorithm
495(2)
10.10.2 Applications
497(1)
10.11 Colliding Bodies Optimization
497(6)
10.11.1 Inspiration and Algorithm
497(5)
10.11.2 Applications
502(1)
10.12 Ray Optimization (RO) Algorithm
503(4)
10.12.1 Inspiration and Algorithm
503(4)
10.12.2 Applications
507(1)
10.13 Extremal Optimization (EO) Algorithm
507(4)
10.13.1 Inspiration and Algorithm
507(2)
10.13.2 Variants of EO
509(1)
10.13.3 Applications
510(1)
10.14 Particle Collision Algorithm
511(2)
10.14.1 Inspiration and Algorithm
511(2)
10.14.2 Applications
513(1)
10.15 River Formation Dynamics
513(4)
10.15.1 Inspiration and Algorithm
513(3)
10.15.2 Applications
516(1)
10.16 Water Cycle Algorithm
517(7)
10.16.1 Inspiration and Algorithm
517(6)
10.16.2 Variants of WCA
523(1)
10.16.3 Applications
523(1)
10.17 Artificial Chemical Process Algorithm
524(6)
10.17.1 Inspiration and Algorithm
524(4)
10.17.2 Applications
528(2)
10.18 Artificial Chemical Reaction Optimization Algorithm
530(3)
10.18.1 Inspiration and Algorithm
530(1)
10.18.2 Applications
531(2)
10.19 Chemical Reaction Algorithm
533(4)
10.19.1 Inspiration and Algorithm
533(3)
10.19.2 Applications
536(1)
10.20 Gases Brownian Motion Optimization (GBMO) Algorithm
537(4)
10.20.1 Inspiration and Algorithm
537(3)
10.20.2 Applications
540(1)
10.21 Conclusion
541(1)
References
541(12)
Appendix A: Vector and Matrix 553(8)
Appendix B: Random Numbers 561(2)
Appendix C: Chaotic Maps 563(4)
Appendix D: Optimization 567(4)
Appendix E: Probability Distribution Function 571(4)
Index 575
Nazmul Siddique obtained Dipl.-Ing degree from Dresden University of Technology, Germany in Cybernetics, M. Sc. in Computer Science from Bangladesh University of Engineering and Technology, and PhD in intelligent control from the Department of Automatic Control and Systems Engineering, University of Sheffield. Dr Siddique is a Lecturer in the School of Computing and Intelligent Systems, Ulster University. He has published over 150 research papers including three books. He has been involved in organizing many national and international conferences. He is on the editorial board of the Journal of Behavioral Robotics, Engineering Letters, International Journal of Machine Learning and Cybernetics, International Journal of Applied Pattern Recognition, International Journal of Advances in Robotics Research and also on the editorial advisory board of the International Journal of Neural Systems. He is a senior member of the IEEE, and a member of executive committee of IEEE SMC UK-RI Chapter.

Hojjat Adeli is Professor of Civil, Environmental, and Geodetic Engineering, and by courtesy Professor of Biomedical Engineering, Biomedical Informatics, Neuroscience, and Neurology at The Ohio State University. He has authored over 550 research publications including 15 books since he received his Ph.D. from Stanford University in 1976. He has presented Keynote Lectures at 103 conferences held in 43 different countries. Among his numerous awards include the University Distinguished Scholar Award "in recognition of extraordinary accomplishment in research and scholarship," the Peter L. and Clara M. Scott Award for Excellence in Engineering Education and Charles E. MacQuigg Outstanding Teaching Award" from The Ohio State University, the ASCE Construction Management Award, a Special Medal in Recognition of Outstanding Contribution to the Development of Computational Intelligence from The Polish Neural Network Society, Eduardo Renato Caianiello Award for Excellence in Scientific Research from Italian Society of Neural Networks, an Honorary Doctorate from Vilnius Gediminas Technical University, Lithuania, and membership in the Spanish Royal Engineering Society. In 2010, he was profiled as Engineering Legend in the ASCE journal of Leadership and Management in Engineering. He is a Distinguished Member of ASCE, and a Fellow of AAAS, IEEE, AIMBE, and the American Neurological Association.
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