Nature-inspired Algorithms and Applied Optimization

Preface......Page 6
Contents......Page 8
Contributors......Page 10
 1 Introduction......Page 13
  2.1 The Essence of an Algorithm......Page 14
  2.2 Optimization......Page 15
  2.4 Heuristics and Metaheuristics......Page 16
 3 Nature-Inspired Optimization Algorithms......Page 17
  3.2 Ant Colony Optimization......Page 18
  3.3 Particle Swarm Optimization......Page 19
  3.5 Bat Algorithm......Page 20
  3.6 Firefly Algorithm......Page 21
  3.7 Cuckoo Search......Page 22
  3.9 Other Algorithms......Page 23
  4.1 Characteristics of Nature-Inspired Algorithms......Page 24
  4.2 Self-organization......Page 25
  4.3 Exploration and Exploitation......Page 26
  4.5 Biased Monto Carlo......Page 27
  4.6 Random Walks......Page 28
  4.7 No Free Lunch Theorems......Page 29
  5.1 Fixed-Point Theory......Page 30
  5.2 Dynamic System......Page 31
  5.3 Markov Chain Theory......Page 32
  5.5 Filter Theory......Page 33
  5.7 Statistical Analysis and Other Approaches......Page 34
 6 Conclusions......Page 35
 References......Page 36
 1 Introduction......Page 38
  2.1 Functions......Page 39
 3 Optimisation Algorithms......Page 40
  3.2 Optimisation Algorithms......Page 41
  3.4 Representing Optimisation Algorithm Behaviour......Page 42
  3.5 Paths down Trees......Page 44
 4 No Free Lunch......Page 46
  5.1 k-Step No Free Lunch......Page 48
  5.3 Stochastic No Free Lunch......Page 51
  6.1 Optimisation Algorithms Are Bijections......Page 52
  6.2 Representation Invariance......Page 53
  6.4 Focused No Free Lunch......Page 54
  6.6 Restricted Metric No Free Lunch......Page 55
  6.7 Multi-objective No Free Lunch......Page 57
  6.8 Block Uniform Distributions......Page 58
 7 Comparing Optimisers After No Free Lunch......Page 59
 8 Metaheuristic Optimisation After NFL......Page 60
 References......Page 61
 1 Introduction......Page 63
  2.1 Standard Cuckoo Search......Page 64
  2.3 Simplified Cuckoo Search......Page 66
 3 Markov Chains and Convergence Criteria......Page 67
  4.2 Markov Chain Model for Cuckoo Search......Page 69
  4.3 Global Convergence of Cuckoo Search......Page 72
 5 Validation by Numerical Experiments......Page 74
 6 Conclusions......Page 75
 References......Page 76
On Efficiently Solving the Vehicle Routing Problem with Time Windows Using the Bat Algorithm with Random Reinsertion Operators......Page 78
 1 Introduction......Page 79
 2 Background......Page 80
 3 Bat Algorithm......Page 84
 4 Vehicle Routing Problem with Time Windows......Page 85
  5.1 An Evolutionary Discrete Bat Algorithm......Page 86
  5.2 Description of the Bat Movement Operator......Page 88
 6 Experimentation......Page 90
 7 Conclusions......Page 95
 References......Page 96
Variants of the Flower Pollination Algorithm: A Review......Page 99
 1 Introduction......Page 100
 2 Flower Pollination and Flower Pollination Algorithm......Page 102
  2.1 FPA in Optimization Context: Nature's Inspiration......Page 103
  2.2 Flower Pollination Algorithm......Page 104
 3 Recent FPA Variants......Page 106
  3.1 Modified Versions of FPA......Page 107
  3.2 Hybridized Versions of FPA......Page 110
  3.4 Parameters Setting Versions of FPA......Page 115
 4 Applications of Flower Pollination Algorithm......Page 116
 5 Critical Analysis of FPA Variants......Page 118
 6 Conclusions and Future Directions......Page 120
 References......Page 121
 1 Introduction......Page 127
  2.1 Complex Numbers......Page 129
  2.2 Hypercomplex Numbers......Page 130
  3.1 Hypercomplex Tools......Page 132
  3.3 Data Structures......Page 133
  3.4 Model Files......Page 134
  4.1 Function Optimization......Page 136
  4.2 Toy Example......Page 137
 5 Methodology and Experiments......Page 139
  5.1 Benchmarking Functions......Page 140
  5.2 Experimental Setup......Page 141
  5.3 Experiments......Page 142
 References......Page 154
Lévy Flight-Driven Simulated Annealing  for B-spline Curve Fitting......Page 156
 1 Introduction......Page 157
  2.1 Basic Definitions......Page 158
  2.2 Data Fitting......Page 159
  3.1 Basic Principles......Page 160
  3.3 Lévy SA Algorithm......Page 161
  4.1 Outline of the Method......Page 165
  4.3 Knot Allocation......Page 167
  4.4 Control Net Computation......Page 168
 5 Experimental Results......Page 169
  5.2 Example 2: High-Density Silhouette of a Cat......Page 170
  5.3 Example 3: Low-Density Silhouette of a Cat......Page 171
  5.5 Computational Issues......Page 172
 6 Conclusions and Future Work......Page 173
 References......Page 174
 1 Introduction......Page 177
 2 Flower Pollination Algorithm......Page 178
  3.1 Chemical Engineering......Page 180
  3.2 Civil Engineering......Page 181
  3.3 Energy and Power Systems......Page 186
  3.4 Mechanical Engineering......Page 188
  3.6 Computer Science......Page 189
 4 Conclusions......Page 190
 References......Page 191
 Abstract......Page 195
 1 Introduction......Page 196
 2 Description of the Standard BA......Page 197
  3.1 Hybrid Variants......Page 199
  3.2 Adaptive Parameters......Page 201
  3.4 Chaotic Sequences......Page 202
  3.5 Bio-Inspired Improvement......Page 203
  3.6 Others......Page 205
 4 The Directional Bat Algorithm......Page 206
  5.1 Unconstrained Optimization......Page 210
  5.2 Constrained Engineering Problem......Page 214
 References......Page 218
 1 Introduction......Page 223
 2 Related Work......Page 225
 3 Flower Pollination Algorithm (FPA) with Selected Applications......Page 226
  3.1 FPA Applied for Feature Selection......Page 227
  4.1 Assessment Indicators......Page 229
  4.2 Datasets......Page 232
  4.3 FPA for Feature Selection Using Classification Data......Page 233
  4.4 FPA for Feature Selection Using Regression Data......Page 236
  4.5 FPA for Knapsack Problem......Page 238
 5 Conclusions......Page 245
 References......Page 248
 1 Introduction......Page 250
  2.1 The Standard Firefly Algorithm......Page 251
  2.2 Special Cases of FA......Page 253
 3 Why the Firefly Algorithm Works?......Page 254
 5 Variants of FA......Page 256
 6 Applications of FA and Its Variants......Page 258
 7 Conclusions and Future Directions......Page 259
 References......Page 260
 1 Introduction......Page 265
 2 Firefly Algorithm for Optimization......Page 266
 3 Modelling to Generate Alternatives......Page 269
 4 FA-Based Simultaneous MGA Computational Algorithm......Page 271
 5 Computational Testing of Simultaneous MGA Algorithm......Page 274
 References......Page 276
 1 Introduction......Page 278
  2.1 Diversity Techniques in Wireless Communications......Page 280
  2.2 MIMO Systems and Space-Time-Frequency Coding......Page 282
  2.3 Cooperative Diversity Protocols and Techniques......Page 283
  2.4 Network Coding Techniques......Page 286
 3 Network Coding in Wireless Butterfly Networks......Page 288
 4 Hybrid Automatic Repeat Request with Incremental Redundancy Protocol and Energy-Delay Tradeoff......Page 289
  4.1 Energy-Delay Tradeoff in Point-to-Point Wireless Links......Page 290
  4.2 Energy-Delay Tradeoff in Wireless Butterfly Networks......Page 291
  4.3 Analysis of EDTs in WBNs......Page 294
 5 Relay Placement in Wireless Butterfly Networks......Page 301
  5.1 Equal Power Allocation at Sources......Page 303
  5.2 Unequal Power Allocation at Sources......Page 304
 6 Relay Placement in Wireless Multicast Networks......Page 306
 References......Page 309
 1 Introduction......Page 315
  1.1 Exploration or Diversification......Page 316
  1.2 Exploitation or Intensification......Page 317
  2.1 Chaotic Bat Algorithm......Page 318
  2.4 The BA with Mutation......Page 319
  2.8 The Double-Subpopulation Lévy Flight Bat Algorithm......Page 320
   3.1.1 Objective Function......Page 321
   3.1.3 Practical Operating Constraints of Generators......Page 322
   3.1.5 Ramp Rate Limits......Page 323
  3.2 Implementation of the BA to ED Problem......Page 324
  4.1 Structural Optimization......Page 325
  4.3 Electrical Power Systems......Page 326
  4.4 Applications in Other Areas......Page 328
 5 Conclusion......Page 329
 References......Page 330