Nature-Inspired Algorithms and Applications

S. Balamurugan, PhD is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents. Anupriya Jain, PhD is an associate professor at the Manav Rachna International Institute of Research and Studies, Faridabad, Haryana. Sachin Sharma, PhD is an assistant professor in computer applications at the Manav Rachna International Institute of Research and Studies, Faridabad, India. He has published more than 30 research papers in different areas of technology and has been a part of two patents as well. Dinesh Goyal, PhD is the Director at the Poornima Institute of Engineering and Technology, Jaipur, India. His research interests are related to information & network security, image processing, data analytics, and cloud computing, and has published more than 60 research articles. Sonia Duggal, PhD is an associate professor at the Manav Rachna International Institute of Research and Studies, Faridabad, Haryana. Seema Sharma is an assistant professor at the Manav Rachna International Institute of Research and Studies, Faridabad, India.

Preface xv

1 Introduction to Nature-Inspired Computing 1
N.M. Saravana Kumar, K. Hariprasath, N. Kaviyavarshini and A. Kavinya

1.1 Introduction 1

1.2 Aspiration From Nature 2

1.3 Working of Nature 3

1.4 Nature-Inspired Computing 4

1.4.1 Autonomous Entity 5

1.5 General Stochastic Process of Nature-Inspired Computation 6

1.5.1 NIC Categorization 8

1.5.1.1 Bioinspired Algorithm 9

1.5.1.2 Swarm Intelligence 10

1.5.1.3 Physical Algorithms 11

1.5.1.4 Familiar NIC Algorithms 12

References 30

2 Applications of Hybridized Algorithms and Novel Algorithms in the Field of Machine Learning 33
P. Mary Jeyanthi and A. Mansurali

2.1 Introduction of Genetic Algorithm 33

2.1.1 Background of GA 35

2.1.2 Why Natural Selection Theory Compared With the Search Heuristic Algorithm? 35

2.1.3 Working Sequence of Genetic Algorithm 35

2.1.3.1 Population 35

2.1.3.2 Fitness Among the Individuals 36

2.1.3.3 Selection of Fitted Individuals 36

2.1.3.4 Crossover Point 37

2.1.3.5 Mutation 37

2.1.4 Application of Machine Learning in GA 38

2.1.4.1 Genetic Algorithm Role in Feature Selection for ML Problem 38

2.1.4.2 Traveling Salesman Problem 39

2.1.4.3 Blackjack—A Casino Game 40

2.1.4.4 Pong Against AI—Evolving Agents (Reinforcement Learning) Using GA 41

2.1.4.5 SNAKE AI—Game 41

2.1.4.6 Genetic Algorithm’s Role in Neural Network 42

2.1.4.7 Solving a Battleship Board Game as an Optimization Problem Which Was Initially Released by Milton Bradley in 1967 43

2.1.4.8 Frozen Lake Problem From OpenAI Gym 43

2.1.4.9 N-Queen Problem 44

2.1.5 Application of Data Mining in GA 44

2.1.5.1 Association Rules Generation 44

2.1.5.2 Pattern Classification With Genetic Algorithm 45

2.1.5.3 Genetic Algorithms in Stock Market Data Mining Optimization 46

2.1.5.4 Market Basket Analysis 46

2.1.5.5 Job Scheduling 46

2.1.5.6 Classification Problem 47

2.1.5.7 Hybrid Decision Tree—Genetic Algorithm to Data Mining 47

2.1.5.8 Genetic Algorithm—Optimization of Data Mining in Education 47

2.1.6 Advantages of Genetic Algorithms 47

2.1.7 Genetic Algorithms Demerits in the Current Era 48

2.2 Introduction to Artificial Bear Optimization (ABO) 50

2.2.1 Bear’s Nasal Cavity 52

2.2.2 Artificial Bear ABO Gist 54

2.2.3 Implementation Based on Requirement 58

2.2.3.1 Market Place 58

2.2.3.2 Industry-Specific 58

2.2.3.3 Semi-Structured or Unstructured Data 59

2.2.4 Merits of ABO 60

2.3 Performance Evaluation 61

2.4 What is Next? 62

References 63

3 Efficiency of Finding Best Solutions Through Ant Colony Optimization (ACO) Technique 67
K. Sasi Kala Rani and N. Pooranam

3.1 Introduction 68

3.1.1 Example of Optimization Process 69

3.1.2 Components of Optimization Algorithms 70

3.1.3 Optimization Techniques Based on Solutions 70

3.1.3.1 Optimization Techniques Based on Algorithms 72

3.1.4 Characteristics 73

3.1.5 Classes of Heuristic Algorithms 74

3.1.6 Metaheuristic Algorithms 75

3.1.6.1 Classification of Metaheuristic Algorithms: Nature-Inspired vs. Non-Nature–Inspired 75

3.1.6.2 Population-Based vs. Single-Point Search (Trajectory) 75

3.1.7 Data Processing Flow of ACO 76

3.2 A Case Study on Surgical Treatment in Operation Room 77

3.3 Case Study on Waste Management System 80

3.4 Working Process of the System 81

3.5 Background Knowledge to be Considered for Estimation 82

3.5.1 Heuristic Function 83

3.5.2 Functional Approach 85

3.6 Case Study on Traveling System 85

3.7 Future Trends and Conclusion 87

References 88

4 A Hybrid Bat-Genetic Algorithm–Based Novel Optimal Wavelet Filter for Compression of Image Data 89
Renjith V. Ravi and Kamalraj Subramaniam

4.1 Introduction 90

4.2 Review of Related Works 91

4.3 Existing Technique for Secure Image Transmission 93

4.4 Proposed Design of Optimal Wavelet Coefficients for Image Compression 93

4.4.1 Optimized Transformation Module 94

4.4.1.1 DWT Analysis and Synthesis Filter Bank 94

4.4.2 Compression and Encryption Module 100

4.4.2.1 SPIHT 100

4.4.2.2 Chaos-Based Encryption 102

4.5 Results and Discussion 104

4.5.1 Experimental Setup and Evaluation Metrics 104

4.5.2 Simulation Results 107

4.5.2.1 Performance Analysis of the Novel Filter KARELET 107

4.5.3 Result Analysis Proposed System 108

4.6 Conclusion 134

References 135

5 A Swarm Robot for Harvesting a Paddy Field 137
N. Pooranam and T. Vignesh

5.1 Introduction 137

5.1.1 Working Principle of Particle Swarm Optimization 138

5.1.2 First Case Study on Birds Fly 138

5.1.3 Operational Moves on Birds Dataset 138

5.1.4 Working Process of the Proposed Model 141

5.2 Second Case Study on Recommendation Systems 142

5.3 Third Case Study on Weight Lifting Robot 145

5.4 Background Knowledge of Harvesting Process 149

5.4.1 Data Flow of PSO Process 150

5.4.2 Working Flow of Harvesting Process 151

5.4.3 The First Phase of Harvesting Process 151

5.4.4 Separation Process in Harvesting 152

5.4.5 Cleaning Process in the Field 152

5.5 Future Trend and Conclusion 155

References 155

6 Firefly Algorithm 157
Anupriya Jain, Seema Sharma and Sachin Sharma

6.1 Introduction 158

6.2 Firefly Algorithm 160

6.2.1 Firefly Behavior 160

6.2.2 Standard Firefly Algorithm 161

6.2.3 Variations in Light Intensity and Attractiveness 163

6.2.4 Distance and Movement 164

6.2.5 Implementation of FA 165

6.2.6 Special Cases of Firefly Algorithm 166

6.2.7 Variants of FA 168

6.3 Applications of Firefly Algorithm 170

6.3.1 Job Shop Scheduling 170

6.3.2 Image Segmentation 171

6.3.3 Stroke Patient Rehabilitation 172

6.3.4 Economic Emission Load Dispatch 172

6.3.5 Structural Design 173

6.4 Why Firefly Algorithm is Efficient 174

6.4.1 FA is Not PSO 176

6.5 Discussion and Conclusion 176

References 177

7 The Comprehensive Review for Biobased FPA Algorithm 181
Meenakshi Rana

7.1 Introduction 182

7.1.1 Stochastic Optimization 183

7.1.2 Robust Optimization 183

7.1.3 Dynamic Optimization 184

7.1.4 Alogrithm 184

7.1.5 Swarm Intelligence 185

7.2 Related Work to FPA 185

7.2.1 Flower Pollination Algorithm 187

7.2.2 Versions of FPA 190

7.2.3 Methods and Description 190

7.2.3.1 Reproduction Factor 193

7.2.3.2 Levy Flights 193

7.2.3.3 User-Defined Parameters 195

7.2.3.4 Psuedo Code for FPA 195

7.2.3.5 Comparative Studies for FPA 196

7.2.3.6 Working Environment 197

7.2.3.7 Improved Versions of FPA 197

7.3 Limitations 202

7.4 Future Research 202

7.5 Conclusion 204

References 204

8 Nature-Inspired Computation in Data Mining 209
Aditi Sharma

8.1 Introduction 209

8.2 Classification of NIC 211

8.2.1 Swarm Intelligence for Data Mining 211

8.2.1.1 Swarm Intelligence Algorithm 212

8.2.1.2 Applications of Swarm Intelligence in Data Mining 214

8.2.1.3 Swarm-Based Intelligence Techniques 214

8.3 Evolutionary Computation 227

8.3.1 Genetic Algorithms 227

8.3.1.1 Applications of Genetic Algorithms in Data Mining 228

8.3.2 Evolutionary Programming 228

8.3.2.1 Applications of Evolutionary Programming in Data Mining 229

8.3.3 Genetic Programming 229

8.3.3.1 Applications of Genetic Programming in Data Mining 229

8.3.4 Evolution Strategies 230

8.3.4.1 Applications of Evolution Strategies in Data Mining 231

8.3.5 Differential Evolutions 231

8.3.5.1 Applications of Differential Evolution in Data Mining 231

8.4 Biological Neural Network 232

8.4.1 Artificial Neural Computation 232

8.4.1.1 Neural Network Models 232

8.4.1.2 Challenges of Artificial Neural Network in Data Mining 233

8.4.1.3 Applications of Artificial Neural Network in Data Mining 233

8.5 Molecular Biology 233

8.5.1 Membrane Computing 233

8.5.2 Algorithm Basis 234

8.5.3 Challenges of Membrane Computing in Data Mining 234

8.5.4 Applications of Membrane Computing in Data Mining 234

8.6 Immune System 235

8.6.1 Artificial Immune System 235

8.6.1.1 Artificial Immune System Algorithm (Enhanced) 236

8.6.1.2 Challenges of Artificial Immune System in Data Mining 236

8.6.1.3 Applications of Artificial Immune System in Data Mining 237

8.7 Applications of NIC in Data Mining 237

8.8 Conclusion 238

References 238

9 Optimization Techniques for Removing Noise in Digital Medical Images 243
D. Devasena, M. Jagadeeswari, B. Sharmila and K. Srinivasan

9.1 Introduction 244

9.2 Medical Imaging Techniques 245

9.2.1 X-Ray Images 245

9.2.2 Computer Tomography Imaging 245

9.2.3 Magnetic Resonance Images 246

9.2.4 Positron Emission Tomography 246

9.2.5 Ultrasound Imaging Techniques 246

9.3 Image Denoising 247

9.3.1 Impulse Noise and Speckle Noise Denoising 247

9.4 Optimization in Image Denoising 249

9.4.1 Particle Swarm Optimization 250

9.4.2 Adaptive Center Pixel Weighted Median Exponential Filter 250

9.4.3 Hybrid Wiener Filter 251

9.4.4 Removal of Noise in Medical Images Using Particle Swarm Optimization 252

9.4.4.1 Curvelet Transform 252

9.4.4.2 PSO With Curvelet Transform and Hybrid Wiener Filter 253

9.4.5 DFOA-Based Curvelet Transform and Hybrid Wiener Filter 255

9.4.5.1 Dragon Fly Optimization Algorithm 255

9.4.5.2 DFOA-Based HWACWMF 256

9.5 Results and Discussions 257

9.5.1 Simulation Results 257

9.5.2 Performance Metric Analysis 257

9.5.3 Summary 263

9.6 Conclusion and Future Scope 264

References 265

10 Performance Analysis of Nature-Inspired Algorithms in Breast Cancer Diagnosis 267
K. Hariprasath, S. Tamilselvi, N. M. Saravana Kumar, N. Kaviyavarshini and S. Balamurugan

10.1 Introduction 268

10.1.1 NIC Algorithms 268

10.2 Related Works 270

10.3 Dataset: Wisconsin Breast Cancer Dataset (WBCD) 274

10.4 Ten-Fold Cross-Validation 275

10.4.1 Training Data 275

10.4.2 Validation Data 275

10.4.3 Test Data 276

10.4.4 Pseudocode 276

10.4.5 Advantages of K-Fold or 10-Fold Cross-Validation 276

10.5 Naive Bayesian Classifier 276

10.5.1 Pseudocode of Naive Bayesian Classifier 278

10.5.2 Advantages of Naive Bayesian Classifier 278

10.6 K-Means Clustering 279

10.7 Support Vector Machine (SVM) 280

10.8 Swarm Intelligence Algorithms 282

10.8.1 Particle Swarm Optimization 283

10.8.2 Firefly Algorithm 285

10.8.3 Ant Colony Optimization 287

10.9 Evaluation Metrics 288

10.10 Results and Discussion 289

10.11 Conclusion 291

References 292

11 Applications of Cuckoo Search Algorithm for Optimization Problems 295
Akanksha Deep and Prasant Kumar Dash

11.1 Introduction 296

11.2 Related Works 298

11.3 Cuckoo Search Algorithm 299

11.3.1 Biological Description 300

11.3.2 Algorithm 300

11.4 Applications of Cuckoo Search 304

11.4.1 In Engineering 305

11.4.1.1 Applications in Mechanical Engineering 305

11.4.2 In Structural Optimization 308

11.4.2.1 Test Problems 308

11.4.3 Application CSA in Electrical Engineering, Power, and Energy 308

11.4.3.1 Embedded System 308

11.4.3.2 PCB 309

11.4.3.3 Power and Energy 309

11.4.4 Applications of CS in Field of Machine Learning and Computation 310

11.4.5 Applications of CS in Image Processing 311

11.4.6 Application of CSA in Data Processing 311

11.4.7 Applications of CSA in Computation and Neural Network 312

11.4.8 Application in Wireless Sensor Network 313

11.5 Conclusion and Future Work 314

References 315

12 Mapping of Real-World Problems to Nature-Inspired Algorithm Using Goal-Based Classification and TRIZ 317
Palak Sukharamwala and Manojkumar Parmar

12.1 Introduction and Background 318

12.2 Motivations Behind NIA Exploration 319

12.2.1 Prevailing Issues With Technology 319

12.2.1.1 Data Dependencies 319

12.2.1.2 Demand for Higher Software Complexity 320

12.2.1.3 NP-Hard Problems 320

12.2.1.4 Energy Consumption 321

12.2.2 Nature-Inspired Algorithm at a Rescue 321

12.3 Novel TRIZ + NIA Approach 322

12.3.1 Traditional Classification 322

12.3.1.1 Swarm Intelligence 322

12.3.1.2 Evolution Algorithm 323

12.3.1.3 Bio-Inspired Algorithms 324

12.3.1.4 Physics-Based Algorithm 324

12.3.1.5 Other Nature-Inspired Algorithms 324

12.3.2 Limitation of Traditional Classification 324

12.3.3 Combined Approach NIA + TRIZ 325

12.3.3.1 TRIZ 325

12.3.3.2 NIA + TRIZ 325

12.3.4 End Goal–Based Classification 326

12.4 Examples to Support the TRIZ + NIA Approach 327

12.4.1 Fruit Optimization Algorithm to Predict Monthly Electricity Consumption 327

12.4.2 Bat Algorithm to Model River Dissolved Oxygen Concentration 332

12.4.3 Genetic Algorithm to Tune the Structure and Parameters of a Neural Network 333

12.5 A Solution of NP-H Using NIA 335

12.5.1 The 0-1 Knapsack Problem 335

12.5.2 Traveling Salesman Problem 337

12.6 Conclusion 338

References 338

Index 341