| book details |

Advances in Neural Network Optimization: Metaheuristic Algorithms and Applications

Edited by Pushan Kumar Dutta, Edited by Pronaya Bhattacharya, Edited by Sushil Kumar Singh, Edited by Udit Mamodiya, Edited by P William

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normal price: R 5 144.95

Price: R 4 629.95

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| book description |

Advances in Neural Network Optimization: Metaheuristic Algorithms and Applications introduces readers to one of the most important challenges in modern artificial intelligence: how to make neural networks faster, smarter, more accurate, and more efficient. Written for a wide academic and professional readership, the book explains how nature-inspired optimization can improve intelligent systems across science, engineering, healthcare, energy, and industry. The volume brings together advanced studies on neural network optimization, metaheuristic algorithms, and their practical applications in emerging intelligent systems. It covers foundational and contemporary approaches such as Grey Lag Goose Optimization, Grey Goose Optimization, whale optimization, swarm intelligence, evolutionary computation, quantum-inspired optimization, reinforcement learning-based neural architecture search, gradient-free learning, and hybrid hyperparameter optimization. The chapters examine how these techniques can be used to improve neural network training, architecture design, pruning, quantization, regularization, automated machine learning, and physically constrained neural networks. The book also extends these methods into applied domains including smart energy, manufacturing, agriculture, cloud-based organizational transformation, communication networks, medical imaging, lung cancer detection, dysarthric speech classification, machine translation, electric motor design, federated learning, resource utilization prediction, and sustainable computation. By combining theory, algorithmic development, surveys, comparative evaluations, and application-focused research, the book provides a broad and integrated view of optimization in next-generation AI systems. The originality of this book lies in its strong connection between metaheuristic theory and real-world neural network applications. It demonstrates how alternative optimization methods can overcome the limits of conventional gradient-based approaches, especially in complex, nonlinear, resource-constrained, and high-dimensional problems. The book will be particularly useful for researchers, postgraduate students, AI developers, data scientists, engineers, and professionals working on intelligent optimization, sustainable AI, and advanced machine learning systems.