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Advanced Concepts in Grey Wolf Optimizer

Leading the Pack in Advanced Optimization

  • 1st Edition - November 1, 2026
  • Latest edition
  • Editor: Seyedali Mirjalili
  • Language: English

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 method… Read more

Description

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.

Key features

  • 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

Readership

Computer Science researchers, artificial intelligence researchers, and researchers and practitioners working in the fields of data science, machine learning, and optimization. The primary audience also includes data analysts and software engineers

Table of contents

Part 1. Advanced Theory and Methodology

1. Optimizing for the Future: Grey Wolf Algorithm Applications in Emerging Fields

2. GWO and MOGWO in Engineering Optimization: Case Studies and Practical Insights

3. Multi-Objective Parameter Optimisation of External Gear Pumps for Industrial Applications: The GWO Advantage

4. Integrating Fuzzy Logic with Grey Wolf Optimizer for Reinforced Cement Concrete (RCC) Design Optimization

5. 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 Problems

7. Grey Wolf Optimizer-Based Adaptive Neuro-Fuzzy Inference System for Estimating the Shear Strength of Reinforced Concrete Shear Walls

Part 2. Scalable and High-Performance Computing

8. Advances in Grey Wolf Optimizer: Variants and Applications

9. Navigating the Future: Advancements and Emerging Trends in Grey Wolf Optimization (GWO)

10. Grey Wolf Optimized Xception Model for Enhanced Deepfake Detection

11. Integration of Grey Wolf Optimization and Its Variants with Machine Learning

12. Grey Wolf Optimizer for Hyperparameter Tuning in Machine Learning and AI Techniques: A Nature-Inspired Approach

13. Hybrid Fitness Grey Wolf Optimizer (Fitness GWO) and Sine-Cosine Algorithm with Its Applications in Machine Learning

Part 3. Hybridization with Next-Gen AI Paradigms

14. Large Language Models and Multi-Objective Grey Wolf Optimizer

15. Large Language Model-Based Grey Wolf Optimiser for Supply Chain

16. Gray Wolf Optimizer Combined with Large Language Models for Design Optimization of a Photonic Crystal Filter

17. Optimizing Transformer Model Performance through GWO (Grey Wolf Optimizer)-Based Hyperparameter Tuning

18. Advancing and Investigating Optimization in Graph Neural Networks: Comparative Understandings on GWO-GNN and CDDO-GNN

Part 4. Engineering, Energy, and Infrastructure

19. Current Developments in Multi-Objective Grey Wolf Optimization: A Review of Algorithms and Applications, Modifications, Challenges, and Future Directions

20. Energy Minimisation Strategy of Industrial Methanol Reactor Using Non-Dominated Sorting Grey Wolf Optimizer Algorithm

21. Multi-Objective Grey Wolf Optimizer for Optimal Allocation and Sizing of Energy Storage Systems in Distribution Networks

22. Grey Wolf Optimizer for Civil Engineering: Practical Implementations, Lessons Learned, and Future Directions

23. Optimizing Satellite Attitude Control Controller Gains: A Multi-Objective Approach Using Grey Wolf Optimization

24. Developed Multi-Objective Grey Wolf Optimizer for Optimal Design of Standalone PV Systems

25. Prediction of Compressive Strength of Geopolymer Concrete Using GWO-ANN Hybrid Model

26. Multi-Strategy-Based Grey Wolf Optimization for Spacecraft Design Problems

27. Forecasting Solar Radiation Using a Hybrid Grey Wolf and Water Whale Plant Optimizer

28. Gray Wolf Optimizer: A New-Generation Tool for Studying Solution Existence in Modern Engineering Problems

29. Damage Detection of Truss Structures Using Grey Wolf Optimizer: A State-of-the-Art Review

Part 5. Digital Systems and Cybersecurity

30. Blockchain Scaling Problems Based on Grey Wolf Optimization

31. A Real-World Integrated Process Planning and Scheduling Problem Solved with an Adapted Multi-Objective Grey Wolf Optimizer

32. Hybrid GWO-Based Optimization for Efficient Task Scheduling in IoT-Fog Environments

33. An Efficient Clustering-Based Task Scheduling Using K-Means Grey Wolf Optimizer in IoT Integrated Edge-Cloud Framework

34. Enhancing IoT Intrusion Detection Systems with Grey Wolf Optimizer and Machine Learning

35. 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 Systems

37. An Ensemble Method Based on Grey Wolf Optimizer for Hyperparameter Optimization in Missing Data Management in the IoMT

38. Grey Wolf Optimization in Robotics: Cutting-Edge Innovations and Future Opportunities

39. Hybrid CNN and Gray Wolf Optimizer for American Sign Language Classification

Part 6. Life Sciences, Health, and Bioinformatics

40. Intelligent Diagnosis of Liver Disorder with Neuro-Fuzzy Grey Wolf Optimization: A Hybrid Approach

41. CNN Hyperparameter Optimization for COVID-19 Detection in Chest X-Ray Images Using Improved Grey Wolf Optimizer

42. Advancing Population Initialization in Grey Wolf Optimization: A Study on Improving Neural Network Performance for Medical Predictions

43. Gray Wolf Optimization Algorithm in Bioinformatics

44. Hybrid Grey Wolf Optimizer Techniques for Optimal Feature Selection in High-Dimensional Biomedical Data

45. Grey Wolf Optimizer-Based Convolutional Neural Network Model for Diagnosis of Retinal Detachment Diseases through Retinal Fundus Images

46. Quantum-Behaved Grey Wolf Optimization for Precise Segmentation of Kidney Stone CT Images

Part 7. Climate, Environment, and Emerging Paradigms

47. Optimizing Artificial Neural Networks with Grey Wolf Optimizer to Improve Imputation Accuracy of Daily Rainfall Data

48. Integrating Grey Wolf Optimizer with AI and ML Models for Accurate Wind Speed Prediction

49. Enhancing Grey Wolf Optimization Using Reinforcement Learning

Product details

  • Edition: 1
  • Latest edition
  • Published: November 1, 2026
  • Language: English

About the editor

SM

Seyedali Mirjalili

Dr. Seyedali Mirjalili is a Professor and globally renowned leader in artificial intelligence and optimization, 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 and Optimization 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 research publications, 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 The Australian for five years, his insights have earned significant media attention, which showcases his influence as a global thought leader.
Affiliations and expertise
Professor and Founding Director, Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Ultimo, NSW, Australia