Optimization Algorithms Using AI

"Optimization Algorithms Using AI" is a comprehensive, practical, and student-centric textbook designed to introduce the synergistic relationship between Artificial Intelligence and the field of optimization. It meticulously bridges the gap between theoretical concepts and their real-world applications, equipping students and professionals with the skills needed to solve some of the most challenging problems in science, engineering, and business.


Key Features of the Book:


1. Beginner to Advanced Trajectory: The book caters to all levels of learners, starting with basic definitions and concluding with an advanced capstone project.
2. Simple Language and Intuitive Examples: Complex mathematical and algorithmic concepts are explained in the simplest possible language, supported by easy-to-understand, real-life examples.
3. Hands-On Practical Approach: Each chapter includes code snippets (primarily in Python), practical exercises, and implementation guides to ensure a "learning by doing" experience.
4. Real-World Case Studies: Every algorithmic chapter features a relevant case study (e.g., optimizing a travel route, scheduling tasks, etc.) to demonstrate the algorithm's practical utility.
5. Comprehensive Capstone Project: The final chapter is a complete, live DIY project that guides the reader through building an end-to-end AI optimization solution, including full source code and step-by-step explanations.
6. Logical and Structured Flow: The ten chapters are arranged in a logical sequence, building knowledge incrementally from foundational concepts to advanced applications and practical implementation.


To Whom This Book Is For:


This book is primarily intended for:

1. B.Tech and M.Tech Computer Science/IT Students: It is strictly aligned with the curricula of major international universities, making it an ideal primary textbook for courses on AI, Soft Computing, or Optimization Techniques.
2. University Professors and Educators: The book's logical structure, chapter-wise case studies, and hands-on labs provide a complete teaching resource.
3. Researchers and Academics: It serves as a strong foundational reference for those exploring novel hybrid optimization models.
4. Industry Professionals and Data Scientists: Professionals working in logistics, finance, manufacturing, and other data-heavy domains will find practical, ready-to-implement solutions and frameworks.
5. Self-Learners and Enthusiasts: Anyone with a basic programming knowledge and a keen interest in AI can use this book for self-study.