Preface xv 1 Introduction to Nature-Inspired Computing 1N.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 33P. 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 67K. 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 89Renjith 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 137N. 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 157Anupriya 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 181Meenakshi 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 209Aditi 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 243D. 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 267K. 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 295Akanksha 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 317Palak 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

The book’s unified approach of balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Inspired by the world around them, researchers are gathering information that can be developed for use in areas where certain practical applications of nature-inspired computation and machine learning can be applied. This book is designed to enhance the reader’s understanding of this process by portraying certain practical applications of nature-inspired algorithms (NIAs) specifically designed to solve complex real-world problems in data analytics and pattern recognition by means of domain-specific solutions. Since various NIAs and their multidisciplinary applications in the mechanical engineering and electrical engineering sectors; and in machine learning, image processing, data mining, and wireless networks are dealt with in detail in this book, it can act as a handy reference guide. Among the subjects of the 12 chapters are: A novel method based on TRIZ to map real-world problems to nature problemsApplications of cuckoo search algorithm for optimization problemsPerformance analysis of nature-inspired algorithms in breast cancer diagnosisNature-inspired computation in data miningHybrid bat-genetic algorithm–based novel optimal wavelet filter for compression of image dataEfficiency of finding best solutions through ant colony optimization techniquesApplications of hybridized algorithms and novel algorithms in the field of machine learning. Audience: Researchers and graduate students in mechanical engineering, electrical engineering, machine learning, image processing, data mining, and wireless networks will find this book very useful.