The Grey Wolf Optimizer

Overview

Search Algorithms and Optimization techniques are the engines of most Artificial Intelligence techniques and Data Science. The Grey Wolf Optimizer (GWO) is a leading AI search technique known for its efficiency and wide application.

Syllabus

• Introduction to Optimization and Search Algorithms
Overview of Optimization in AI and Data Science
Importance of Search Algorithms
• Basics of Metaheuristics
Definition and Characteristics
Comparison with Deterministic Approaches
• Foundations of Grey Wolf Optimizer (GWO)
Inspiration from Grey Wolf Hunting Behavior
Basic Components and Structure of GWO
• Mathematical Model of GWO
Formulation of Leadership Hierarchy
Social Behavior Modeling
• Implementation of GWO
Pseudocode of GWO
Step-by-step Execution Process
• Parameter Tuning and Control in GWO
Key Parameters and Their Impact
Strategies for Efficient Parameter Setting
• Comparison with Other Optimization Techniques
Difference between GWO and Genetic Algorithms, PSO
Advantages and Limitations of GWO
• Applications of Grey Wolf Optimizer
GWO in Engineering and Design Problems
GWO in Data Science and Machine Learning
• Advanced Variations and Improvements of GWO
Modified and Hybridized GWO Variants
Recent Research and Developments
• Hands-on Session: Implementing GWO
Setting Up a Coding Environment
Example Problems and Solutions Using GWO
• Real-world Case Studies
Successful Implementation in Industrial Projects
Analysis of GWO in Different Domains
• Future Directions and Research Opportunities
Potential Enhancements
Emerging Trends in Optimization Techniques

Taught by

Seyedali Mirjalili

Subjects

Data Science