Advanced Concepts in Grey Wolf Optimizer

Nyhet

Leading the Pack in Advanced Optimization

Häftad, Engelska, 2026

2 549 kr

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Advanced Concepts in Grey Wolf Optimizer: Leading the Pack in Advanced Optimization provides in-depth coverage of recent theoretical advancements in GWO, as well as advanced methods to handle issues such as multiple objectives, constraints, binary variables, large search spaces, dynamic goals, and uncertain data. This book assumes familiarity with optimization fundamentals and therefore dives directly into multi-objective, constrained, binary and dynamic-environment variants, as well as GWO-ML/LLM hybrids. Extensive real-world case studies in areas such as energy systems, supply-chain design, LLM fine-tuning, robotics, and finance ensure that both scholars and engineers can translate the material into deployable solutions. The authors present important new theories, hybrids with Machine Learning/Deep Learning, and hybrid methods that increase GWO’s performance. The use of generative AI to improve this algorithm and make it more generic is also explored, along with diverse applications across multiple fields to illustrate the practical utility and versatility of the methods presented. Written by some of the world’s most highly cited researchers in the field of artificial intelligence, algorithms, and machine learning, the book serves as an advanced resource for researchers and practitioners interested in applying and developing the Grey Wolf Optimizer.

Presents the use of new AI tools such as Generative AI (GenAI), Large Language Models (LLM), and Data Processing (DP) with the Grey Wolf Optimizer, showing readers how these technologies can improve and expand GWO capabilities
Provides a comprehensive overview of the latest GWO modifications and hybrid approaches, including methods to handle complex challenges such as multi-objective tasks, constraints, noisy data, and dynamic conditions
Includes many practical examples and real-world case studies from areas such as engineering, healthcare, finance, and robotics, which helps readers apply advanced GWO methods in various scientific and industrial settings

Produktinformation

Utgivningsdatum2026-08-01
Mått216 x 276 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor300
FörlagElsevier Science
ISBN9780443457265

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Dr. Seyedali Mirjalili is a Professor and globally renowned leader in artificial intelligence andoptimization, recognized as the No. 1 AI researcher on Stanford University’s prestigious World’s Top Scientists list since 2023. He founded the Centre for Artificial Intelligence Research andOptimization in 2019 and serves as a Professor of AI at Torrens University Australia, with distinguished professorships in Hungary and the Czech Republic. With more than 600 researchpublications, 130,000 citations, and an H-index of 125, Prof. Mirjalili is among the top 1% of highly cited researchers worldwide. His contributions include developing AI algorithms widely applied in science and industry and delivering influential talks, including a TED Talk on AI's transformative potential. Prof. Mirjalili is a strong advocate for responsible and inclusive AI, and he has collaborated with industry and government on ethical AI tools. As a senior member of IEEE and an editor for leading AI journals, he significantly contributed to the advancements of fundamental and applied research in the field. Recognized as a top research leader by TheAustralian for five years, his insights have earned significant media attention, which showcaseshis influence as a global thought leader.

Part 1. Advanced Theory and Methodology1. Optimizing for the Future: Grey Wolf Algorithm Applications in Emerging Fields2. GWO and MOGWO in Engineering Optimization: Case Studies and Practical Insights3. Multi-Objective Parameter Optimisation of External Gear Pumps for Industrial Applications: The GWO Advantage4. Integrating Fuzzy Logic with Grey Wolf Optimizer for Reinforced Cement Concrete (RCC) Design Optimization5. Optimizing Crop Selection through Soil Data Analysis using Grey Wolf Optimizer (GWO) with a Multi-objective Clustering Algorithm (MCA)6. The Role of Grey Wolf Optimizer in Solving Multi-Objective Problems7. Grey Wolf Optimizer-Based Adaptive Neuro-Fuzzy Inference System for Estimating the Shear Strength of Reinforced Concrete Shear WallsPart 2. Scalable and High-Performance Computing8. Advances in Grey Wolf Optimizer: Variants and Applications9. Navigating the Future: Advancements and Emerging Trends in Grey Wolf Optimization (GWO)10. Grey Wolf Optimized Xception Model for Enhanced Deepfake Detection11. Integration of Grey Wolf Optimization and Its Variants with Machine Learning12. Grey Wolf Optimizer for Hyperparameter Tuning in Machine Learning and AI Techniques: A Nature-Inspired Approach13. Hybrid Fitness Grey Wolf Optimizer (Fitness GWO) and Sine-Cosine Algorithm with Its Applications in Machine LearningPart 3. Hybridization with Next-Gen AI Paradigms14. Large Language Models and Multi-Objective Grey Wolf Optimizer15. Large Language Model-Based Grey Wolf Optimiser for Supply Chain16. Gray Wolf Optimizer Combined with Large Language Models for Design Optimization of a Photonic Crystal Filter17. Optimizing Transformer Model Performance through GWO (Grey Wolf Optimizer)-Based Hyperparameter Tuning18. Advancing and Investigating Optimization in Graph Neural Networks: Comparative Understandings on GWO-GNN and CDDO-GNNPart 4. Engineering, Energy, and Infrastructure19. Current Developments in Multi-Objective Grey Wolf Optimization: A Review of Algorithms and Applications, Modifications, Challenges, and Future Directions20. Energy Minimisation Strategy of Industrial Methanol Reactor Using Non-Dominated Sorting Grey Wolf Optimizer Algorithm21. Multi-Objective Grey Wolf Optimizer for Optimal Allocation and Sizing of Energy Storage Systems in Distribution Networks22. Grey Wolf Optimizer for Civil Engineering: Practical Implementations, Lessons Learned, and Future Directions23. Optimizing Satellite Attitude Control Controller Gains: A Multi-Objective Approach Using Grey Wolf Optimization24. Developed Multi-Objective Grey Wolf Optimizer for Optimal Design of Standalone PV Systems25. Prediction of Compressive Strength of Geopolymer Concrete Using GWO-ANN Hybrid Model26. Multi-Strategy-Based Grey Wolf Optimization for Spacecraft Design Problems27. Forecasting Solar Radiation Using a Hybrid Grey Wolf and Water Whale Plant Optimizer28. Gray Wolf Optimizer: A New-Generation Tool for Studying Solution Existence in Modern Engineering Problems29. Damage Detection of Truss Structures Using Grey Wolf Optimizer: A State-of-the-Art ReviewPart 5. Digital Systems and Cybersecurity30. Blockchain Scaling Problems Based on Grey Wolf Optimization31. A Real-World Integrated Process Planning and Scheduling Problem Solved with an Adapted Multi-Objective Grey Wolf Optimizer32. Hybrid GWO-Based Optimization for Efficient Task Scheduling in IoT-Fog Environments33. An Efficient Clustering-Based Task Scheduling Using K-Means Grey Wolf Optimizer in IoT Integrated Edge-Cloud Framework34. Enhancing IoT Intrusion Detection Systems with Grey Wolf Optimizer and Machine Learning35. Intrusion Detection in Drone Networks Based on Grey Wolf Optimizer and Artificial Neural Networks (GWO-ANN)36. Grey Wolf Optimizer for Hyperparameter Tuning of Deep Neural Networks for Network Intrusion Detection Systems37. An Ensemble Method Based on Grey Wolf Optimizer for Hyperparameter Optimization in Missing Data Management in the IoMT38. Grey Wolf Optimization in Robotics: Cutting-Edge Innovations and Future Opportunities39. Hybrid CNN and Gray Wolf Optimizer for American Sign Language ClassificationPart 6. Life Sciences, Health, and Bioinformatics40. Intelligent Diagnosis of Liver Disorder with Neuro-Fuzzy Grey Wolf Optimization: A Hybrid Approach41. CNN Hyperparameter Optimization for COVID-19 Detection in Chest X-Ray Images Using Improved Grey Wolf Optimizer42. Advancing Population Initialization in Grey Wolf Optimization: A Study on Improving Neural Network Performance for Medical Predictions43. Gray Wolf Optimization Algorithm in Bioinformatics44. Hybrid Grey Wolf Optimizer Techniques for Optimal Feature Selection in High-Dimensional Biomedical Data45. Grey Wolf Optimizer-Based Convolutional Neural Network Model for Diagnosis of Retinal Detachment Diseases through Retinal Fundus Images46. Quantum-Behaved Grey Wolf Optimization for Precise Segmentation of Kidney Stone CT ImagesPart 7. Climate, Environment, and Emerging Paradigms47. Optimizing Artificial Neural Networks with Grey Wolf Optimizer to Improve Imputation Accuracy of Daily Rainfall Data48. Integrating Grey Wolf Optimizer with AI and ML Models for Accurate Wind Speed Prediction49. Enhancing Grey Wolf Optimization Using Reinforcement Learning