Preface xxv Part I: Foundations and Introduction to Nature-Inspired Intelligence 1 1 Introduction to Nature-Inspired Intelligence 3 Simarpreet Kaur and Vikas Wasson 1.1 Overview of Nature-Inspired Computing 4 1.2 The Need for Bio-Inspired Solutions 9 1.3 Key Characteristics of Nature-Inspired Algorithms 18 1.4 Conclusion and Future Scope 20 2 Exploring Swarm Intelligence: A Comparative Analysis of Nature-Inspired Optimization Techniques 27 Inderdeep Kaur and Aleem Ali 2.1 Introduction to Swarm Intelligence 28 2.2 Fundamentals of Swarm Intelligence 30 2.3 Ant Colony Optimization (ACO) 34 2.
4 Particle Swarm Optimization (PSO) 39 2.5 Grey Wolf Optimizer (GWO) 44 2.6 Comparative Analysis of Swarm Intelligence Algorithms 49 2.7 Applications of Swarm Intelligence Algorithms in Real-World Problems 53 2.8 Challenges and Future Research Directions 58 2.9 Conclusion 59 3 Swarm Dynamics in Optimization: A Deep Dive into PSO 63 Benjamin Franklin S., Justin Jayaraj K., Monisha A.
, Balasubramaniam V., Sasi Kala and N.S. Kavitha 3.1 Particle Swarm Optimization (PSO) 64 3.2 Engineering Designs in PSO 69 3.3 Variants of PSO 75 3.4 Swarm Intelligence in PSO 84 3.
5 PSO in Healthcare and Logistic 86 3.6 Enhancements in PSO for Improved Performance 89 4 Genetic Algorithms: Fundamentals and Applications 97 Satya Reddy Satti, Chanchal Alam, Ajay Sharma and Shamneesh Sharma 4.1 Fundamentals of Genetic Algorithms 99 4.2 Mathematical Foundations of Genetic Algorithms 102 4.3 Schema Theorem and Building Block Hypothesis 102 4.4 Multi-Objective Optimization 103 4.5 Applications of Genetic Algorithms 106 4.6 Challenges and Mitigations 110 4.
7 Practical Implementation for Genetic Algorithms 112 4.8 Future Directions in Genetic Algorithm Research 114 5 Challenges and Future Directions in Nature-Inspired Intelligence 121 Thayanithi C.A., Elipe Arjun and Priyanka Singh 5.1 Introduction 122 5.2 Contemporary Challenges 124 5.3 Emerging Technologies and Trends 130 5.4 Future Research Directions 137 5.
5 Implementation Strategies 142 5.6 Impact Analysis 148 5.7 Future Recommendations 152 5.8 Conclusion 155 Part II: Methods and Hybrid Models 161 6 Hybrid Swarm Intelligence for Enhancing Optimization through Multi Swarm and Quantum Inspired Models in Decision Making and Robotics 163 Barakkath Nisha U., Yasir Abdullah R., Sindhu V., Raihana A. and Anitha G.
6.1 Introduction 164 6.2 Background and Related Work 167 6.3 Framework of Hybrid Swarm Intelligence 171 6.4 Applications of Hybrid Swarm Intelligence 175 6.5 Experimental Results and Performance Analysis 179 6.6 Conclusion 187 7 Swarm Intelligence and Differential Evolution in Robotics and Decision-Making 191 Devendra Babu Pesarlanka, Abhinav Kumar, Ajay Sharma, Arun Malik and Shamneesh Sharma 7.1 Introduction 192 7.
2 Fundamentals of Swarm Intelligence 194 7.3 Key Swarm Intelligence Algorithms 196 7.4 Particle Swarm Optimization (PSO) 200 7.5 Applications of Swarm Intelligence in Robotics 204 7.6 Swarm Intelligence in Decision-Making 207xiv Contents 7.7 Challenges and Future Directions 210 7.8 Conclusion 213 8 Hybrid Nature-Inspired Systems: A Computational Intelligence Perspective 219 Anitha Subbarayan 8.1 Evolutionary Computation for Global Search Optimization 220 8.
2 Swarm Intelligence in Local Search and Refinement 224 8.3 Neuro-Evolutionary Models for Adaptive Learning 230 8.4 Hybridization Strategies for Balancing Exploration and Exploitation 237 8.5 Co-Evolutionary and Memetic Algorithms 238 8.6 Applications of Hybrid Nature-Inspired Systems 239 8.7 Performance Metrics and Computational Efficiency of Hybrid Nature-Inspired Systems 243 9 Optimizing Engineering Systems: Differential Evolution Algorithm and Hybrid Approaches for PID Controller 249 G. Saravanan, C. Pazhanimuthu, P.
N. Senthil Prakash and N.R. Wilfred Blessing 9.1 Introduction 250 9.2 Related Works 252 9.3 Algorithms 254 9.4 System Model 261 9.
5 Simulation Results and Discussion 271 10 Novel Aspects of Ant Colony Optimization and Particle Swarm Optimization 279 Rohan Gupta and Gurpreet Singh 10.1 MANET Routing Strategies 280 10.2 Routing Protocols 281 10.3 Ant Based Routing Protocols 286 10.4 PSO Routing Protocols 287 10.5 Hybrid Routing Protocols 288 10.6 Results and Discussion 289 10.7 Conclusion 292 11 Physics-Inspired Algorithms: Applications in Energy and Environmental Systems 297 Naman Srivastava, Samyak Varia, Scaria Alex, Aswathy K.
Cherian, Ashwini S. and Arshey M. 11.1 Introduction 298 11.2 Foundations of Physics-Inspired Algorithms (PIAs) 301 11.3 Application of Physics-Inspired Algorithms (PIAs) in Energy Systems [1492 and 0%] 308 11.4 Application of PIAs in Environmental Systems 316 11.5 Case Studies and Real-Life Implementations 322 11.
6 Challenges and Way Forward 325 11.7 Conclusion 329 Part III: Applications Across Domains 333 12 Optimization-Driven Deep CNN with PFCM Clustering for Enhanced MRI-Based Brain Tumor Detection 335 P. Sathish, Sashikanth Reddy Avula and Channabasava 12.1 Introduction 336 12.2 Related Work 337 12.3 Proposed Exponential Cuckoo-Based DCNN for Automatic Brain Tumor Classification 339 12.4 Discussion of Results 344 12.5 Summary 353 13 Explainable AI and Ensemble Learning for Genetic Disorder Diagnosis Advancing Accuracy and Interpretability in Healthcare Predictions 357 Ishdeep and Neetu Rani 13.
1 Introduction 358 13.2 Literature Review 359 13.3 Materials and Methods 364 13.4 Results and Discussion 375 13.5 Conclusion 380 13.6 Future Scope 381 14 Optimizing Complex Weights of Linear Antenna Array for Combating Real World Wireless Traffic Congestion 385 Surekha Rani and Himanshu Sharma 14.1 Introduction 385 14.2 Problem Formulation 386xx Contents 14.
3 Simulation and Results 392 14.4 Conclusion and Future Scope 402 15 Nature-Inspired Intelligence for Enhanced Disease Detection in Medical Image Analysis 405 R. Karthick Manoj, Aasha Nandhini S. and D. Lakshmi 15.1 Introduction 406 15.2 Related Work 407 15.3 Proposed Methodology 409 15.
4 Result and Discussion 417 15.5 Conclusion and Future Work 425 16 Nature-Inspired Hybrid Model for Dysgraphia Diagnosis in Educational Settings 429 A. Devi, B. Elizebeth Caroline, J. Vidhya, D. Sathish Kumar, T.D. Subha and L.
Manimegalai 16.1 Introduction 430 16.2 Related Works 434 16.3 Proposed Hybrid Model 440 16.4 Feature Selection Using ACO 449 16.5 Results and Discussions 451 16.6 Conclusion 457 17 Particle Swarm Optimization for Effective Feature Selection in Smart Logistics 461 Asha K. and Nakul Ramesh Varma 17.
1 Introduction 461 17.2 Particle Swarm Optimization 462 17.3 Literature Survey 469 17.4 Computational Analysis on Realtime-Case 471 17.5 Legal and Ethical Considerations 473 17.6 Methodology 474 17.7 Implementation and Results 476 17.8 Conclusion 477 18 The Integration of IoT and Blockchain for Enhanced Security and Real-Time Updates 483 Priya Batta and Abhishek Kumar 18.
1 Introduction 483 18.2 Related Works 488 18.3 Proposed Methodology 491 18.4 Results and Discussions 493 18.5 Conclusion and Future Scope 494 19 Advancing Rehabilitation with Virtual Reality 497 Charu Chhabra, Fowquiya, Sohrab A. Khan and Ifra Aman 19.1 Introduction to Virtual Reality 497 19.2 Methodology 498 19.
3 Literature 498 19.4 Discussion 508 19.5 Result 509 19.6 Conclusion 509 Part IV: Case Studies and Specific Implementations 515 20 AI for Preserving Indian Knowledge Systems and Philosophy 517 Aditya Atal, Shaurya Sharma and G.Y. Rajaa Vikhram 20.1 Introduction 518 20.2 AI in Preserving Ancient Hindu Texts and Literature 518 20.
3 AI-Driven Religious Chatbots and Q&A Systems 520 20.4 AI and Digital Preservation of Oral Traditions and Folklore 521 20.5 AI in Ayurveda and Traditional Healing 522 20.6 AI in Yoga and Meditation Guidance 523 20.7 AI-Powered Knowledge Systems for Hindu Ethics and Philosophy 524 20.8 Role of AI in Hindu Astrology and Vedic Mathematics 524 20.9 Ethical and Theological Considerations in AI-Based Hindu Studies 525 20.10 Role of Blockchain and Quantum Computing in Hindu Knowledge Systems 525 20.
11 Future Scope and Challenges 533 20.12 Conclusion and Research Directions 538 20.13 Research Gaps and Areas for Further Exploration 540 21 Nature-Inspired Algorithms and Their Applications: A Healthcare Case Study with the Bee Algorithm 543 Puneet Kumar and Deepika Kumar 21.1 Introduction 544 21.2 Classification of Nature-Inspired Algorithms 548 21.3 Bees Algorithm: Foundation 551 21.4 Case Study: Bees Algorithm in Healthcare 556 References 559 Index 561.