Nature-Inspired Optimizers : Theories, Literature Reviews and Applications

This book covers the conventional and most recent theories and applications in the area of evolutionary algorithms, swarm intelligence, and meta-heuristics. Each chapter offers a comprehensive description of a specific algorithm, from the mathematical model to its practical application. Different kind of optimization problems are solved in this book, including those related to path planning, image processing, hand gesture detection, among others. All in all, the book offers a tutorial on how to design, adapt, and evaluate evolutionary algorithms. Source codes for most of the proposed techniques have been included as supplementary materials on a dedicated webpage. Preface......Page 8 Contents......Page 10 Contributors......Page 15 Introduction to Nature-Inspired Algorithms......Page 17 Reference......Page 21 1 Introduction......Page 22 2 Inspiration......Page 23 3 Mathematical Model......Page 24 3.1 Construction Phase......Page 25 3.2 Pheromone Phase......Page 26 3.4 Max-Min Ant System......Page 27 3.6 Continuous Ant Colony......Page 28 4 Application of ACO in AUV Path Planning......Page 29 References......Page 35 Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Networks......Page 37 1 Introduction......Page 38 2 Ant Lion Optimizer......Page 39 3 Literature Review......Page 42 4 Perceptron Neural Networks......Page 44 5 ALO for Training MLPs......Page 45 6 Results and Discussions......Page 47 7 Conclusions and Future Directions......Page 54 References......Page 56 Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection......Page 61 1 Introduction......Page 62 2 Feature Selection Problem......Page 63 3 Dragonfly Algorithm......Page 64 4 Literature Review......Page 67 5 Binary DA (BDA)......Page 68 5.1 BDA-Based Wrapper Feature Selection......Page 69 6 Results and Discussions......Page 70 6.1 Results and Analysis......Page 71 7 Conclusions and Future Directions......Page 74 References......Page 76 1 Introduction......Page 82 2.1 Inspiration......Page 83 2.3 Initial Population......Page 84 2.4 Selection......Page 85 2.5 Crossover (Recombination)......Page 86 2.6 Mutation......Page 88 3 Experiments......Page 89 References......Page 96 1 Introduction......Page 99 2 Grey Wolf Optimizer......Page 100 2.1 Encircling Prey......Page 101 3 Literature Review......Page 104 3.1 Different Variants of GWO......Page 105 4 Performance Analysis of GWO......Page 106 4.1 Convergence Behaviour of GWO......Page 107 4.2 Changing the Main Controlling Parameter of GWO......Page 109 5 Marine Propeller Design Using GWO......Page 111 6 Conclusions......Page 114 References......Page 115 Grasshopper Optimization Algorithm: Theory, Literature Review, and Application in Hand Posture Estimation......Page 118 1 Introduction......Page 119 2 Grasshopper Optimization Algorithm......Page 120 3.1 Different Variants of GOA......Page 123 3.2 Applications of GOA......Page 124 4.1 Testing GOA on Some Benchmark Functions......Page 125 4.2 Applying GOA to the Problem of Hand Posture Estimation......Page 127 5 Conclusion......Page 130 References......Page 131 Multi-verse Optimizer: Theory, Literature Review, and Application in Data Clustering......Page 134 1 Introduction......Page 135 2 Multi-verse Optimizer......Page 136 3 Literature Review......Page 137 3.1 MVO Variants......Page 138 3.2 MVO Applications......Page 139 4 Application of MVO in Solving Data Clustering Problems......Page 140 5 Implementation and Results......Page 142 5.1 Evaluation Measures......Page 143 5.2 Results and Analysis......Page 144 6 Conclusions and Future Directions......Page 147 References......Page 148 1 Introduction......Page 153 2.1 Background and Motivation......Page 155 2.4 The MFO Algorithm......Page 156 3 Literature Review of MFO......Page 161 3.2 Applications......Page 162 4 MFO for Optimal Control Problems......Page 163 4.1 Problem Definition......Page 164 4.2 Collocation......Page 165 5.1 Example 1. Rotational/Translational Actuator......Page 166 5.2 Example 2. F-8 Aircraft......Page 169 6 Conclusion......Page 170 References......Page 171 1 Introduction......Page 177 2 Particle Swarm Optimization......Page 178 3.1 Exploration and Exploitation in PSO......Page 182 3.2 2D Airfoil Design Using PSO......Page 187 References......Page 193 1 Introduction......Page 195 2 Extreme Learning Machines......Page 197 3 Salp Swarm Algorithm......Page 198 4 Literature Review......Page 200 5 Proposed SSA-ELM Approach......Page 201 6 Experiments and Results......Page 202 References......Page 206 1 Introduction......Page 210 2 Sine Cosine Algorithm......Page 212 3.1 Variants......Page 215 3.2 Application......Page 216 4.1 Solving Benchmark Functions Using SCA......Page 217 4.2 Designing Bend Photonic Crystal Waveguides Using SCA......Page 220 5 Conclusion......Page 222 References......Page 223 1 Introduction......Page 227 2 Whale Optimization Algorithm......Page 229 3.1 Variants......Page 232 3.2 Applications......Page 233 4.1 Results of WOA on Benchmark Functions......Page 234 4.2 Results of WOA When Designing Photonic Crystal Filters......Page 237 References......Page 241