Nature-inspired computing: physics- and chemistry-based algorithms
Gespeichert in:
| Beteiligte Personen: | , |
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| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton
CRC Press, Taylor & Francis Group
[2017]
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| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030320822&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030320822&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Includes bibliographical references and index |
| Umfang: | xxv, 596 pages 27 cm |
| ISBN: | 9781482244823 |
Internformat
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| 100 | 1 | |a Siddique, Nazmul |d 1963- |e Verfasser |0 (DE-588)105972278X |4 aut | |
| 245 | 1 | 0 | |a Nature-inspired computing |b physics- and chemistry-based algorithms |c Nazmul Siddique, Ulster University, UK, Hojjat Adeli, the Ohio State University, USA |
| 264 | 1 | |a Boca Raton |b CRC Press, Taylor & Francis Group |c [2017] | |
| 300 | |a xxv, 596 pages |c 27 cm | ||
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| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Biomathematics | |
| 650 | 4 | |a Computational intelligence | |
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| 700 | 1 | |a Adeli, Hojjat |d 1950- |e Verfasser |0 (DE-588)121399060 |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030320822&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
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Datensatz im Suchindex
| _version_ | 1819339885851443200 |
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| adam_text | Contents
Foreword............................................................................xvii
Preface..............................................................................xix
Acknowledgments....................................................................xxiii
Authors..............................................................................xxv
Chapter 1 Dialectics of Nature: Inspiration for Computing..............................1
1.1 Inspiration from Nature..................................1
1.2 Brief History of Natural Sciences.............................2
1 »2.1 Laws of Motion..............................................3
1.2.2 Law of Gravitation..........................................3
1.2.3 Transformation between Heat and Mechanical Energy...........4
1.2.4 Transformation between Mass and Energy......................7
1.2.5 Light and Optics............................................9
1.2.6 Sound and Acoustics.........................................9
1.2.7 Hydrology and Dynamics.....................................10
1.2.8 Development in Chemistry...................................11
1.2.9 Development in Biological Sciences.........................12
1.3 Traditional Approaches to Search and Optimization....................14
1.3.1 Line Search.............................................. 16
1.3.2 Golden Section Search......................................16
1.3.3 Fibonacci Search...........................................17
1.3.4 Newton’s Method............................................17
1.3.5 Secant Method..............................................17
1.3.6 Gradient-Based Methods.....................................18
1.3.6.1 Descent Methods...................................18
1.3.6.2 Gradient Methods..................................19
1.3.6.3 Steepest Descent Method (or Gradient Descent).....19
1.3.7 Classical Newton’s Method..................................19
1.3.8 Modified Newton’s Method...................................20
1.3.9 Levenberg-Marquardt Modification...........................20
1.3.10 Quasi-Newton Method........................................21
1.3.11 Conjugate Direction Methods................................21
1.3.12 Conjugate Gradient Methods.................................22
1.3.13 BFGS Method................................................23
1.3.14 Deterministic vs Stochastic Algorithms.....................23
1.3.15 Local Search Methods.......................................25
1.3.15.1 Scatter Search....................................26
1.3.15.2 Tabu Search (TS)..................................26
1.3.15.3 Random Search (RS)................................26
1.3.15.4 Downhill Simplex (Nelder-Mead) Method.............26
1.4 Paradigm of NIC....................................................27
1.5 Physics-Based Algorithms......................................... 28
1.6 Chemistry-Based Algorithms.........................................31
1.7 Biology-Based Algorithms...........................................32
1.8 Culture-, Society-, and Civilization-Based Algorithms..............36
1.9 Overview...........................................................37
• •
VII
vni Contents
1.10 Conclusion...........................................................40
References.................................................................40
Chapter 2 Gravitational Search Algorithm.............................................51
2.1 Introduction....................................................... 51
2.2 Physics of Gravity...................................................52
2.2.1 Acceleration of Objects.....................................53
2.2.2 Mass of Moving Objects.......................................53
2.3 Gravitational Search Algorithm.......................................54
2.4 Parameters of GSA....................................................61
2.5 Fitness Function.....................................................62
2.5.1 Fitness Scaling............................................ 62
2.6 Variants of GSA......................................................63
2.6.1 Binary GSA................................................. 63
2.6.2 Chaotic GSA..................................................65
2.6.3 Piece-Wise Linear Chaotic Map and Sequential Quadratic
Programming with GSA.........................................66
2.6.4 GSA with Chaotic Local Search................................67
2.6.5 Discrete GSA.................................................68
2.6.6 Mass-Dispersed GSA...........................................69
2.6.7 Opposition-Based GSA.........................................70
2.6.8 GSA with Wavelet Mutation....................................71
2.6.9 Quantum-Inspired GSA.........................................72
2.6.10 Quantum-Inspired BGSA........................................73
2.6.11 Piece-Wise Function-Based GSA...............................74
2.6.12 Adaptive GSA.................................................75
2.6.13 Mutation-Based GSA........................................ 76
2.6.13.1 Sign Mutation.......................................76
2.6.13.2 Reordering Mutation.................................77
2.6.14 Disruption-Based GSA........................................78
2.6.15 Random Local Extrema-Based GSA..............................79
2.6.16 Modified GSA.................................................80
2.7 Hybrid GSA......................................................... 81
2.7.1 Hybrid PSO-GSA...............................................81
2.7.2 Hybrid GSA-EA................................................84
2.7.3 Hybrid Fuzzy GSA.............................................85
2.7.4 Hybrid QBGSA-K-NN............................................86
2.7.5 Hybrid K-Harmonic Means and GSA..............................86
2.8 Application to Engineering Problems..................................87
2.8.1 Benchmark Function Optimization..............................87
2.8.2 Combinatorial Optimization Problems..........................88
2.8.3 Economic Load Dispatch (ELD) Problem.........................88
2.8.4 Economic and Emission Dispatch (EED) Problem.................89
2.8.5 Optimal Power Flow (OPF) Problem.............................91
2.8.6 Reactive Power Dispatch (RPD) Problem........................92
2.8.7 Energy Management System (EMS)...............................95
2.8.8 Clustering Problem......................................... 96
2.8.9 Classification Problem.......................................98
2.8.10 Feature Subset Selection (FSS)...............................99
2.8.11 Parameter Identification...................................101
Contents ix
2.8.12 Training NNs................................................104
2.8.13 Traveling Salesman Problem (TSP)............................105
2.8.14 Filter Design and Communication Systems.....................106
2.8.15 Unit Commitment Problem (UCP)...............................107
2.8.16 Multiobjective Optimization Problem (MOOP)..................109
2.8.17 Fuzzy Controller Design.....................................109
2.9 Conclusion..........................................................110
References................................................................110
Chapter 3 Central Force Optimization................................................119
3.1 Introduction........................................................119
3.2 Central Force Optimization Metaphor.................................119
3.3 CFO Algorithm.......................................................124
3.4 Parameters of the CFO Algorithm.....................................128
3.5 Decision Space and Probe Distribution...............................129
3.5.1 Standard or Fully Connected.................................129
3.5.2 Linear......................................................130
3.5.3 Mesh........................................................130
3.5.4 Ring........................................................130
3.5.5 Star........................................................130
3.5.6 Static Tree.................................................131
3.5.7 Toroidal....................................................131
3.6 Variants of CFO.....................................................132
3.6.1 Simple CFO..................................................132
3.6.2 Extended CFO................................................133
3.6.3 Pseudo-Random CFO...........................................134
3.6.4 Parameter-Free CFO..........................................135
3.6.5 Improved CFO................................................136
3.6.6 CFO with Acceleration Clipping..............................138
3.6.7 Binary CFO..................................................138
3.6.8 Multistart CFO..............................................140
3.7 Hybrid CFO..........................................................140
3.7.1 Hybrid CFO-Nelder-Mead (CFO-NM).............................140
3.7.2 Hybrid CFO and Intelligent State Space Pruning..............141
3.7.3 Multistart or Modified CFO (MCFO)...........................142
3.7.4 Hybrid CFO and Hill-Climbing................................143
3.8 Applications to Engineering Problems................................143
3.8.1 Electronic Circuit Design...................................143
3.8.2 Antenna Design..............................................144
3.8.3 Benchmark Function Optimization.............................147
3.8.4 Training Neural Network.....................................150
3.8.5 Water Pipe Networks.........................................151
3.8.6 Multiobjective CFO Algorithm................................153
3.9 Convergence of CFO.........................................154
3.10 Conclusion......................................................... 155
References................................................................155
Chapter 4 Electromagnetism-Like Optimization........................................159
4.1 Introduction........................................................159
X
Contents
4.2 EMO Algorithm.......................................................160
4.3 Variants of EMO.....................................................162
4.3.1 EMO Variants Based on Parameters............................163
4.3.1.1 Revised EMO..........................................163
4.3.1.2 Discrete EMO (DEMO)..................................164
4.3.1.3 Opposition-Based EMO.................................166
4.3.1.4 Improved EMO.........................................167
4.3.1.5 Multipopulation EMO..................................167
4.3.1.6 Memory-Based EMO.....................................169
4.3.2 Hybrid EMO with Other Meta-Heuristics.........................169
4.3.2.1 Hybrid Modified EMO and Scatter Search...............169
4.3.2.2 Hybrid EMO and Restarted Arnoldi Algorithm...........170
4.3.2.3 Hybrid EMO and Iterated Swap Procedure...............171
4.3.2.4 Hybrid EMO and SA....................................172
4.3.2.5 Hybrid EMO and Solis-Wets Search.....................173
4.3.2.6 Hybrid EMO and Great Deluge (GD).................174
43.2/7 Hybrid EMO and GA....................................176
4.3.2.8 Species-Based Improved EMO...........................177
4.3.2.9 Hybrid EMO and Davidon-Fletcher-Powell Search.......178
4.3.2.10 Hybrid EMO and PSO...................................179
4.3.2.11 Hybrid EMO and TS....................................180
4.3.2.12 Hybrid EMO and DE....................................181
4.3.2.13 Opposite Sign Test-Based EMO (EMO-OST)..............183
4.4 Applications to Engineering Problems.................................183
4.4.1 Constrained Optimization Problem..............................183
4.4.2 Traveling Salesman Problem.................................. 184
4.4.3 Timetabling Problem...........................................184
4.4.4 Job Shop Scheduling Problem...................................184
4.4.5 Knapsack Problem (KP).........................................185
4.4.6 Set Covering Problem..........................................185
4.4.7 Feature Subset Selection......................................185
4.4.8 Inverse Kinematics Problem in Robotics........................186
4.4.9 Vehicle Routing Problem.......................................186
4.4.10 Maximum Betweenness Problem...................................186
4.4.11 Redundancy Allocation Problem (RAP)...........................186
4.4.12 Uncapacitated Multiple Allocation p-hub Median Problem........187
4.4.13 Resource-Constrained Project Scheduling (RCPS) Problem........188
4.4.14 Multiobjective Optimization Problem...........................189
4.4.15 Other Applications............................................189
4.5 Conclusions..........................................................190
References.................................................................190
Chapter 5 Harmony Search................................................................195
5.1 Introduction.........................................................195
5.2 Harmony in Music.....................................................196
5.3 Musical Improvisation................................................197
5.4 Harmony Memory.......................................................200
5.5 Harmony Search Algorithm.............................................202
5.5.1 Initializing the Optimization Problem and Algorithm Parameters.... 203
5.5.2 Initializing HM...............................................203
Contents
XI
5.5.3 Improvising Harmony from HM..................................203
5.5.3.1 Harmony Memory Consideration.........................204
5.5.3.2 Random Consideration.................................205
5.5.3.3 Pitch Adjustment.....................................205
5.5.4 Updating HM..................................................206
5.5.5 Stopping Condition...........................................206
5.6 Characteristic Features of Parameters in the HSA.....................206
5.7 Variants of the HSA..................................................208
5.7.1 HSA Variants Based on Parameters.............................208
5.7.1.1 Binary Harmony Search (HS)...........................209
5.7.1.2 Improved HS (IHS)....................................210
5.7.1.3 Global Best HS (GHS).................................213
5.7.1.4 Adaptive HS..........................................215
5.7.1.5 Self-Adaptive GHS....................................217
5.7.1.6 Discrete HS........................................ 218
5.7.1.7 Chaotic HS (CHS).....................................219
5.7.1.8 Gaussian HS..........................................220
5.7.1.9 Innovative GHS.......................................222
5.7.1.10 Dynamic/Parameter Adaptive HS.......................223
5.7.1.11 Explorative HS (EHS)................................224
5.7.1.12 Quantum-Inspired HS.................................225
5.7.1.13 Opposition-Based HS.................................228
5.7.1.14 Cellular HS.........................................229
5.7.1.15 Design-Driven HS (DDHS).............................231
5.7.1.16 Island-Based HS.....................................232
5.7.1.17 Harmony Memory (HM) Initialization..................233
5.7.1.18 Grouping HS.........................................234
5.7.1.19 Multiple Pitch Adjustment Rate HS (PAR HS)..........235
5.7.1.20 Geometric Selective HS..............................235
5.7.1.21 Adaptive Binary HS..................................235
5.7.2 HSA Variants Based on Hybridization with Other Methods.......236
5.7.2.1 Hybridizing HS with Other Meta-Heuristic Algorithms..236
5.1.2.2 Hybridizing HS Components into Other
Meta-Heuristic Algorithms...........................254
5.8 Application of HSA to Engineering Problems...........................255
5.8.1 Function and Constrained Optimization Problems...............255
5.8.2 Structural Design Optimization...............................256
5.8.3 Hydrologic Model Optimization................................258
5.8.4 Water Distribution Network (WDN).............................259
5.8.5 Water Pump Switching Problem.................................260
5.8.6 Transmission Network Expansion Planning Problem..........262
5.8.7 Job Shop Scheduling..........................................264
5.8.8 Timetabling and Rostering Problem............................265
5.8.9 Training NN..................................................266
5.8.10 Clustering Problem...........................................268
5.8.11 Combined Heat and Power Economic Dispatch Problem............271
5.8.12 ELD Problem..................................................273
5.8.13 Economic and Emission Dispatch Problem.......................274
5.8.14 MOOP.........................................................275
5.9 Conclusion...........................................................276
References.................................................................277
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Water Drop Algorithm...........................................
6.1 Introduction.............................................
6.2 River Systems............................................
6.2.1 Sediment Production, Transport, and Storage in the
Working River....................................
6.3 Natural WDs..............................................
6.4 WDs Algorithm............................................
6.5 Parameters of WDA........................................
6.6 Convergence Analysis.....................................
6.7 Variants of WDA..........................................
6.7.1 Improved WDA.....................................
6.7.2 Modified WDA.....................................
6.7.3 Adaptive WDA.....................................
6.7.4 WDA Continuous Optimization Algorithm............
6.8 Applications to Engineering Problems.....................
6.8.1 Traveling Salesman Problem.......................
6.8.2 The «-Queen Problem..............................
6.8.3 Multidimensional Knapsack Problem................
6.8.4 Vehicle Routing Problem (VRP)....................
6.8.5 Economic Load Dispatch Problem...................
6.8.6 Combined Economic and Emission Dispatch Problem
6.8.7 Reactive Power Dispatch Problem..................
6.8.8 Vehicle Guidance in Road Graph Networks............
6.8.9 Path Planning....................................
6.8.10 Trajectory Planning..............................
6.8.11 FS...............................................
6.8.12 Automatic Multilevel Thresholding................
6.8.13 Data Aggregation and Routing in Wireless Networks..
6.8.14 Mobile ad hoc Networks (MANET)...................
6.8.15 JSSP.............................................
6.8.16 Web Service Selection............................
6.8.17 Max-Clique Problem...............................
6.8.18 Reservoir Operation Problem......................
6.8.19 Data Clustering..................................
6.8.20 Steiner Tree Problem.............................
6.8.21 Other Applications...............................
6.9 Conclusion...............................................
References.....................................................
Spiral Dynamics Algorithms...........................
7.1 Introduction...................................
7.2 Spiral Phenomena in Nature.....................
7.3 Parametric Representation of Curves............
7.3.1 Parametric Representation of Spirals....
7.3.1.1 Archimedes or Arithmetic Spiral
7.3.1.2 Hyperbolic Spirals.............
7.3.1.3 Farmat’s or Parabolic Spiral...
7.3.1.4 Lituus Spiral..................
7.3.1.5 Clothoid Spirals...............
Contents
XIII
7.4 Logarithmic Spirals................................................337
7.5 Spiral Models......................................................339
7.5.1 2-D Spiral Models......................................... 339
7.5.2 «-Dimensional Spiral Models.................................341
7.6 SpD-Based Optimization Algorithm...................................344
7.6.1 2-D Spiral Dynamics Optimization Algorithm..................344
7.6.2 «-Dimensional SpDO Algorithm................................345
7.6.3 Parameters of SpDO Algorithm................................345
7.7 Variants of SpDO Algorithm.........................................346
7.7.1 Adaptive SpDO Algorithm.....................................346
7.7.1.1 Linear Adaptive SpDO...............................347
7.7.1.2 Quadratic Adaptive SpDO............................348
7.7.1.3 Exponential Adaptive SpDO..........................348
7.7.1.4 Fuzzy Adaptive SpDO................................348
7.7.2 Hybrid SpDO Algorithms......................................349
7.7.2.1 Hybrid Spiral-Dynamics Bacterial-Chemotaxis
Algorithm..........................................349
7.7.2.2 Hybrid Spiral-Dynamics Random-Chemotaxis
Algorithm..........................................350
1.123 Hybrid Spiral-Dynamics Bacterial-Foraging Algorithm...351
7.8 Stability of Spiral Models.........................................354
7.9 Applications to Engineering Problems...............................355
7.9.1 Modeling and Control Design.................................357
7.9.2 Training of Neural Network..................................358
7.9.3 Combined Economic and Emission Dispatch.....................359
7.9.4 Clustering Applications................................... 359
7.9.5 Heat Sink Design............................................360
7.10 Conclusions........................................................360
References...............................................................360
Chapter 8 Simulated Annealing........................................................363
8.1 Introduction.......................................................363
8.2 Principles of Statistical Thermodynamics...........................363
8.3 Annealing Process..................................................364
8.4 SA Algorithm.......................................................365
8.5 Cooling (Annealing) Schedule.......................................367
8.5.1 Monotonic Schedules (or Simple Time Schedule)...............369
8.5.2 Geometric (or Exponential) Cooling Schedule.................372
8.5.3 Adaptive Cooling............................................372
8.5.4 Initial Temperature.........................................374
8.5.5 Final Temperature...........................................375
8.5.6 Stopping Condition..........................................375
8.6 Neighborhoods.................................................... 376
8.7 Variants of SA.....................................................377
8.7.1 Boltzmann Annealing.........................................377
8.7.2 Fast Annealing..............................................377
8.7.3 Very Fast Simulated Reannealing.............................378
8.7.4 Adaptive SA............................................... 378
8.7.5 Discrete SA.................................................379
8.7.6 Coupled SA..................................................379
xiv Contents
8.7.7 Modified SA..................................................381
8.7.8 Corana SA....................................................381
8.7.9 Orthogonal SA.............................................. 382
8.7.10 Chaotic SA...................................................383
8.7.11 Quantum Annealing............................................384
8.8 Hybrid SA...........................................................384
8.8.1 Hybrid SA and GA.............................................384
8.8.2 Hybrid Harmony Search-Based SA.............................385
8.8.3 Hybrid PSO-SA................................................387
8.8.4 Hybrid Ant Colony Optimization and SA........................389
8.8.5 Hybrid ACO, GA, and SA.......................................390
8.8.6 Hybrid DE and SA........................................... 390
8.8.7 Hybrid Artificial Immune System and SA.......................391
8.8.8 Noising Method with SA.......................................392
8.9 Convergence Analysis................................................393
8.10 Application to Engineering Problems.................................394
8.10.1 Travelling Salesman Problem..................................395
8.10.2 Job-Shop Scheduling Problem..................................396
8.10.3 Training NN..................................................399
8.10.4 Clustering Problem...........................................400
8.10.5 Vertex Covering Problem......................................403
8.10.6 Flow Shop Sequencing Problem.................................405
8.10.7 Multiobjective Optimization..................................406
8.11 Conclusion..........................................................406
References................................................................407
Chapter 9 Chemical Reaction Optimization.............................................415
9.1 Introduction........................................................415
9.2 Mechanisms of Chemical Reaction................................... 420
9.3 Chemical Reaction Optimization......................................420
9.4 Features of CRO................................................... 425
9.5 Parameters of CRO................................................. 427
9.5.1 CRO Operators................................................427
9.6 Variants of CRO.....................................................428
9.6.1 Real-Coded CRO (RCCRO).......................................428
9.6.2 Opposition-Based CRO.........................................430
9.6.3 Orthogonal CRO...............................................431
9.6.4 Adaptive Collision CRO.......................................431
9.6.5 Elitist CRO..................................................432
9.6.6 Artificial Chemical Reaction Optimization (ACRO) Algorithm..432
9.7 Hybrid CRO..........................................................433
9.7.1 Hybrid CRO and DE............................................433
9.7.2 Hybrid CRO and PSO......................................... 433
9.7.3 Hybrid CRO and Lin-Kernighan Local Search....................434
9.8 Application of CRO..................................................435
9.8.1 Quadratic Assignment Problem.................................435
9.8.2 Traveling Salesman Problem...................................437
9.8.3 Resource-Constrained Project Scheduling Problem..............437
9.8.4 Economic Load Dispatch Problem...............................438
9.8.5 Optimal Power Flow Problem...................................439
Contents
xv
9.8.6 Training Neural Networks.......................................439
9.8.7 Fuzzy Rules Learning...........................................439
9.8.8 Communications and Networking Problems.........................440
9.8.8.1 Peer-to-Peer Streaming.................................440
9.8.8.2 Cognitive Radio Spectrum Allocation Problem............440
9.8.8.3 Channel Assignment Problem.............................440
9.8.8.4 Network Coding Optimization Problem....................441
9.8.8.5 Bus Sensor Deployment Problems.........................441
9.8.9 Multiobjective Optimization Problems...........................442
9.8.10 Other Applications.............................................442
9.9 Conclusion.............................................................443
References..................................................................443
Chapter 10 Miscellaneous Algorithms.......................................................447
10.1 Introduction...........................................................447
10.2 Big Bang-Big Crunch (BB-BC) Algorithm..............................447
10.2.1 Inspiration and Algorithm......................................447
10.2.2 Applications...................................................450
10.3 Black Hole Algorithm...................................................451
10.3.1 Inspiration and Algorithm......................................451
10.3.2 Applications...................................................455
10.4 Galaxy-Based Search....................................................455
10.4.1 Inspiration and Algorithm......................................455
10.4.1.1 Spiral Chaotic Move....................................456
10.4.1.2 Local Search...........................................457
10.4.2 Applications...................................................459
10.5 Artificial Physics Optimization........................................459
10.5.1 Inspiration and Algorithm......................................459
10.5.2 Vector Model APO...............................................463
10.5.3 Hybrid Vector APO..............................................464
10.5.4 Extended APO Algorithm.........................................465
10.5.5 Local APO Algorithm............................................466
10.5.6 APO with Feasibility-Based Rule................................467
10.5.7 Applications...................................................468
10.6 Space Gravitational Optimization.......................................469
10.6.1 Inspiration and Algorithm......................................469
10.6.2 Modified SGO...................................................472
10.6.3 Consideration of Shape of Universe in SGO......................473
10.6.4 Applications...................................................474
10.7 Integrated Radiation Optimization......................................475
10.7.1 Inspiration and Algorithm.................................... 475
10.7.2 Applications...................................................478
10.8 Gravitational Interactions Optimization................................479
10.8.1 Inspiration and Algorithm.................................... 479
10.8.2 Applications...................................................481
10.9 Charged System Search................................................ 481
10.9.1 Inspiration and Algorithm......................................481
10.9.2 Discrete CSS...................................................486
10.9.3 Chaotic CSS....................................................486
10.9.4 Adaptive CSS...................................................488
XVI
Contents
10.9.5 Magnetic CSS...................................................489
10.9.6 Hybrid CSS.....................................................492
10.9.7 Applications................................................. 493
10.10 Hysteretic Optimization...............................................495
10.10.1 Inspiration and Algorithm.....................................495
10.10.2 Applications..................................................497
10.11 Colliding Bodies Optimization.........................................497
10.11.1 Inspiration and Algorithm.....................................497
10.11.2 Applications..................................................502
10.12 Ray Optimization (RO) Algorithm.......................................503
10.12.1 Inspiration and Algorithm.....................................503
10.12.2 Applications................................................ 507
10.13 Extremal Optimization (EO) Algorithm..................................507
10.13.1 Inspiration and Algorithm.....................................507
10.13.2 Variants of EO.............................................. 509
10.13.3 Applications..................................................510
10.14 Particle Collision Algorithm..........................................511
10.14.1 Inspiration and Algorithm.....................................511
10.14.2 Applications..................................................513
10.15 River Formation Dynamics..............................................513
10.15.1 Inspiration and Algorithm.....................................513
10.15.2 Applications..................................................516
10.16 Water Cycle Algorithm.................................................517
10.16.1 Inspiration and Algorithm................................... 517
10.16.2 Variants of WCA...............................................523
10.16.3 Applications..................................................523
10.17 Artificial Chemical Process Algorithm.................................524
10.17.1 Inspiration and Algorithm.....................................524
10.17.2 Applications..................................................528
10.18 Artificial Chemical Reaction Optimization Algorithm...................530
10.18.1 Inspiration and Algorithm................................... 530
10.18.2 Applications..................................................531
10.19 Chemical Reaction Algorithm...........................................533
10.19.1 Inspiration and Algorithm.....................................533
10.19.2 Applications..................................................536
10.20 Gases Brownian Motion Optimization (GBMO) Algorithm...................537
10.20.1 Inspiration and Algorithm.....................................537
10.20.2 Applications..................................................540
10.21 Conclusion............................................................541
References...................................................................541
Appendix A: Vector and Matrix.............................................................553
Appendix B: Random Numbers................................................................561
Appendix C: Chaotic Maps..................................................................563
Appendix D: Optimization..................................................................567
Appendix E: Probability Distribution Function.............................................571
Index................................................................................. 575
Computer Science Engineering
K23506
=170-1-4022-4402-3
90000
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 explor-
ing 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 endeavor 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 profession-
als, 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 econom-
ics students, as well as those in computer and information sciences.
The book can also serve as a reference for researchers and scientists in the fields of sys-
tem science, natural computing, and optimization.
About the Authors
Nazmul Siddique obtained thr Dipl.-lng degree from Dresden University of Technology,
Germany in Cybernetics, M.Sc. in Computer Science from Bangladesh University of En-
gineering and Technology, and Ph.D. in intelligent control from the Department of Auto-
matic Control and Systems Engineering, University of Sheffield. Dr. Siddique is with the
School of Computing and Intelligent Systems, Ulster University. He has published over
150 research papers and three books.
Hojjat Adeli is Professor of Civil, Environmental, and Geodetic Engineering, and by cour-
tesy Professor of Biomedical Engineering, Biomedical Informatics, Neuroscience, and
Neurology at The Ohio State University. He has authored 570 research publications,
including 16 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. He is a
Distinguished Member of ASCE, and a Fellow of AAAS, IEEE, AIMBE, and the American.
Neurological Association.
6000 Broken Sound Parkway, NW
Suite 300, Boca Raton, FL 33487
711 Third Avenue
New York, NY 1001 7
2 Park Square, Miltqp Park
Abingdon, Oxon OX14 4RN, UK
Taylor Francis Group
an informa business
www.crc press.com
|
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| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 570 - Biology |
| dewey-raw | 570.1/51 |
| dewey-search | 570.1/51 |
| dewey-sort | 3570.1 251 |
| dewey-tens | 570 - Biology |
| discipline | Biologie Informatik |
| format | Book |
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| id | DE-604.BV044927681 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T18:14:29Z |
| institution | BVB |
| isbn | 9781482244823 |
| language | English |
| lccn | 016040264 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030320822 |
| oclc_num | 1017659054 |
| open_access_boolean | |
| owner | DE-703 DE-739 |
| owner_facet | DE-703 DE-739 |
| physical | xxv, 596 pages 27 cm |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | CRC Press, Taylor & Francis Group |
| record_format | marc |
| spellingShingle | Siddique, Nazmul 1963- Adeli, Hojjat 1950- Nature-inspired computing physics- and chemistry-based algorithms Biomathematics Computational intelligence Biomathematik (DE-588)4139408-2 gnd Soft Computing (DE-588)4455833-8 gnd |
| subject_GND | (DE-588)4139408-2 (DE-588)4455833-8 |
| title | Nature-inspired computing physics- and chemistry-based algorithms |
| title_auth | Nature-inspired computing physics- and chemistry-based algorithms |
| title_exact_search | Nature-inspired computing physics- and chemistry-based algorithms |
| title_full | Nature-inspired computing physics- and chemistry-based algorithms Nazmul Siddique, Ulster University, UK, Hojjat Adeli, the Ohio State University, USA |
| title_fullStr | Nature-inspired computing physics- and chemistry-based algorithms Nazmul Siddique, Ulster University, UK, Hojjat Adeli, the Ohio State University, USA |
| title_full_unstemmed | Nature-inspired computing physics- and chemistry-based algorithms Nazmul Siddique, Ulster University, UK, Hojjat Adeli, the Ohio State University, USA |
| title_short | Nature-inspired computing |
| title_sort | nature inspired computing physics and chemistry based algorithms |
| title_sub | physics- and chemistry-based algorithms |
| topic | Biomathematics Computational intelligence Biomathematik (DE-588)4139408-2 gnd Soft Computing (DE-588)4455833-8 gnd |
| topic_facet | Biomathematics Computational intelligence Biomathematik Soft Computing |
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