Day 1: Metaheuristics: A Class of Intelligent Search Methods in AI | Introduction to Optimization

Welcome to Day 1 of our online workshop, "Metaheuristics: A Class of Intelligent Search Methods in AI"!

This session lays the groundwork for understanding intelligent search methods by diving into the fundamentals of optimization. We begin by exploring how everyday problems can be framed using mathematical modeling, introducing core concepts like constraints, objective functions, search space, and feasible solutions.

Through practical examples like the famous Königsberg Bridge Problem and the Chinese Postman Problem, we break down different problem-solving approaches, from simple brute-force to more efficient heuristics. A key focus is on the Traveling Salesman Problem (TSP), which we use to demonstrate the computational limitations of exhaustive search methods and highlight the need for smarter algorithms.

Finally, we classify optimization methods, distinguishing between exact methods that guarantee optimality and approximate methods like heuristics and metaheuristics, setting the stage for the rest of the workshop.

🔗 Workshop Materials & Resources:
Access all slides, code, and supplementary materials here:
https://sites.google.com/view/shubham...
Other Resources: https://sites.google.com/view/shubham...


00:00:00 Start
00:03:59 - Introduction & Welcome: Inaugural session and workshop overview.
00:19:30 - Workshop Logistics: Schedule, materials, and communication.
00:24:19 - Intro to Optimization (Part 1): Learning through problem examples.
00:25:44 - Concept: Mathematical Modeling (Age Problem)
00:29:29 - Concept: Constraints & Objectives (Budget Problem)
00:39:25 - Concept: Brute Force Methods (Diagram Tracing)
00:46:53 - Example: Königsberg Bridge Problem (Network Modeling)
00:54:30 - Example: Chinese Postman Problem
01:05:58 - The Decision-Making Process in Optimization
01:18:32 - Defining Optimization, Search Space & Objective Space
01:26:23 - Example: The Traveling Salesman Problem (TSP)
01:35:29 - Why Brute Force Fails: A TSP Demonstration
01:37:57 - Classification of Optimization Methods (Exact vs. Approximate)
01:45:09 - Heuristics vs. Metaheuristics Explained
01:49:14 - When Should You Use Metaheuristics?
01:53:29 - Brief Introduction to Linear Programming
02:10:05 - Problem-Specific Heuristic: Greedy Algorithm for TSP
02:13:54 - Q&A Session

📌 Topics Covered:
🔹 Workshop Overview (Schedule, Materials, Instructor Info)
🔹 Mathematical Modeling & Problem Solving
✔ Königsberg Bridge Problem
✔ The Chinese Postman Problem
🔹 Introduction to Optimization
✔ Decision-Making Process
✔ Search Space vs Objective Space
🔹 Search Problems & Brute Force Limitations
🔹 TSP & its Applications
✔ Scalability Issues in Enumeration Search
🔹 Classification of Optimization Methods
✔ Exact vs Approximate Methods
🔹 Heuristics vs Metaheuristics
✔ When to Use Metaheuristics?
🔹 Mathematical Programming (LP & Integer Programming)
🔹 Greedy Algorithms in Optimization
✔ Nearest Neighbor Algorithm for TSP (Greedy Approach & Limitations)

🎯 Who Should Watch?
This video is perfect for students, faculty members, researchers, and industry professionals interested in Artificial Intelligence, optimization, and intelligent search algorithms. Whether you're in computer science, engineering, or data science, this session provides a solid foundation.

🎓 About the Workshop:
This four-day online workshop (6th – 9th February 2025) is organized by the Department of Electronics and Communication Engineering, NIT Rourkela, in collaboration with the IEEE Student Chapter, Rourkela Section.

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