Foreword xv Preface xvii 1 Metaheuristic Algorithms and Their Applications in Different Fields: A Comprehensive Review 1Abrar Yaqoob, Navneet Kumar Verma and Rabia Musheer Aziz 1.1 Introduction 2 1.2 Types of Metaheuristic Algorithms 3 1.3 Application of Metaheuristic Algorithms 20 1.4 Future Direction 25 1.5 Conclusion 26 2 A Comprehensive Review of Metaheuristics for Hyperparameter Optimization in Machine Learning 37Ramachandran Narayanan and Narayanan Ganesh 2.1 Introduction 38 2.2 Fundamentals of Hyperparameter Optimization 39 2.3 Overview of Metaheuristic Optimization Techniques 42 2.4 Population-Based Metaheuristic Techniques 43 2.5 Single Solution-Based Metaheuristic Techniques 47 2.6 Hybrid Metaheuristic Techniques 49 2.7 Metaheuristics in Bayesian Optimization 50 2.8 Metaheuristics in Neural Architecture Search 53 2.9 Comparison of Metaheuristic Techniques for Hyperparameter Optimization 55 2.10 Applications of Metaheuristics in Machine Learning 61 2.11 Future Directions and Open Challenges 63 2.12 Conclusion 65 3 A Survey of Computer-Aided Diagnosis Systems for Breast Cancer Detection 73Charu Anant Rajput, Leninisha Shanmugam and Parkavi K. 3.1 Introduction 73 3.2 Procedure for Research Survey 77 3.3 Imaging Modalities and Their Datasets 77 3.4 Research Survey 83 3.5 Conclusion 90 3.6 Acknowledgment 91 4 Enhancing Feature Selection Through Metaheuristic Hybrid Cuckoo Search and Harris Hawks Optimization for Cancer Classification 95Abrar Yaqoob, Navneet Kumar Verma, Rabia Musheer Aziz and Akash Saxena 4.1 Introduction 96 4.2 Related Work 99 4.3 Proposed Methodology 104 4.4 Experimental Setup 115 4.5 Results and Discussion 119 4.6 Conclusion 130 5 Anomaly Identification in Surveillance Video Using Regressive Bidirectional LSTM with Hyperparameter Optimization 135Rajendran Shankar and Narayanan Ganesh 5.1 Introduction 136 5.2 Literature Survey 137 5.3 Proposed Methodology 138 5.4 Result and Discussion 143 5.5 Conclusion 146 6 Ensemble Machine Learning-Based Botnet Attack Detection for IoT Applications 149Suchithra M. 6.1 Introduction 150 6.2 Literature Survey 151 6.3 Proposed System 152 6.4 Results and Discussion 156 6.5 Conclusion 160 7 Machine Learning-Based Intrusion Detection System with Tuned Spider Monkey Optimization for Wireless Sensor Networks 163Ilavendhan Anandaraj and Kaviarasan Ramu 7.1 Introduction 164 7.2 Literature Review 166 7.3 Proposed Methodology 168 7.4 Result and Discussion 173 7.5 Conclusion 177 8 Security Enhancement in IoMT--Assisted Smart Healthcare System Using the Machine Learning Approach 179Jayalakshmi Sambandan, Bharanidharan Gurumurthy and Syed Jamalullah R. 8.1 Introduction 180 8.2 Literature Review 182 8.3 Proposed Methodology 184 8.4 Conclusion 192 9 Building Sustainable Communication: A Game-Theoretic Approach in 5G and 6G Cellular Networks 195Puppala Ramya, Tulasidhar Mulakaluri, Chebrolu Yasmina, Pandi Bindu Madhavi and Vijay Guru Balaji K. S. 9.1 Introduction 196 9.2 Related Works 196 9.3 Methodology 197 9.4 Result 207 9.5 Conclusion 211 10 Autonomous Vehicle Optimization: Striking a Balance Between Cost-Effectiveness and Sustainability 215Vamsidhar Talasila, Sagi Venkata Lakshmi Narasimharaju, Neeli Veda Vyshnavi, Saketh Naga Sreenivas Kondaveeti, Garimella Surya Siva Teja and Kiran Kumar Kaveti 10.1 Introduction 216 10.2 Methods 219 10.3 Results 224 10.4 Conclusions 231 11 Adapting Underground Parking for the Future: Sustainability and Shared Autonomous Vehicles 235Vamsidhar Talasila, Madala Pavan Pranav Sai, Gade Sri Raja Gopala Reddy, Vempati Pavan Kashyap, Gunda Karthik and K. V. Panduranga Rao 11.1 Introduction 236 11.2 Related Works 237 11.3 Methodology 238 11.4 Analysis 245 11.5 Conclusion 250 12 Big Data Analytics for a Sustainable Competitive Edge: An Impact Assessment 253Rajyalakshmi K., Padma A., Varalakshmi M., Suhasini A. and Chiranjeevi P. 12.1 Introduction 254 12.2 Related Works 255 12.3 Hypothesis and Research Model 255 12.4 Results 259 12.5 Conclusion 264 13 Sustainability and Technological Innovation in Organizations: The Mediating Role of Green Practices 267Rajyalakshmi K., Rajkumar G. V. S., Sulochana B., Rama Devi V. N. and Padma A. 13.1 Introduction 268 13.2 Related Work 269 13.4 Discussion 279 13.5 Conclusions 281 14 Optimal Cell Planning in Two Tier Heterogeneous Network through Meta-Heuristic Algorithms 285Sanjoy Debnath, Amit Baran Dey and Wasim Arif 14.1 Introduction 285 14.2 System Model and Formulation of the Problem 288 14.3 Result and Discussion 295 14.4 Conclusion 298 15 Soil Aggregate Stability Prediction Using a Hybrid Machine Learning Algorithm 301M. Balamurugan 15.1 Introduction 302 15.2 Related Works 303 15.3 Proposed Methodology 303 15.4 Result and Discussion 309 15.5 Conclusion 313 References 313 Index 315

The book unlocks the power of nature-inspired optimization in machine learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications. The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex optimization problems. With advancements in machine learning and artificial intelligence, the application of metaheuristic optimization techniques has expanded, demonstrating significant potential in optimizing machine learning models, hyperparameter tuning, and feature selection, among other use-cases. In the industrial landscape, these techniques are becoming indispensable for solving real-world problems in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into machine learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and machine learning represents a pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field. Metaheuristics for Machine Learning: Algorithms and Applications serves as a comprehensive guide to the intersection of nature-inspired optimization and machine learning. Authored by leading experts, this book seamlessly integrates insights from computer science, biology, and mathematics to offer a panoramic view of the latest advancements in metaheuristic algorithms. You’ll find detailed yet accessible discussions of algorithmic theory alongside real-world case studies that demonstrate their practical applications in machine learning optimization. Perfect for researchers, practitioners, and students, this book provides cutting-edge content with a focus on applicability and interdisciplinary knowledge. Whether you aim to optimize complex systems, delve into neural networks, or enhance predictive modeling, this book arms you with the tools and understanding you need to tackle challenges efficiently. Equip yourself with this essential resource and navigate the ever-evolving landscape of machine learning and optimization with confidence. Audience The book is aimed at a broad audience encompassing researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics. The detailed but accessible content makes it a must-have for both academia and industry professionals interested in the optimization aspects of machine learning algorithms.