
Handbook of AI-based Metaheuristics
- 398 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Handbook of AI-based Metaheuristics
About this book
At the heart of the optimization domain are mathematical modeling of the problem and the solution methodologies. The problems are becoming larger and with growing complexity. Such problems are becoming cumbersome when handled by traditional optimization methods. This has motivated researchers to resort to artificial intelligence (AI)-based, nature-inspired solution methodologies or algorithms.
The Handbook of AI-based Metaheuristics provides a wide-ranging reference to the theoretical and mathematical formulations of metaheuristics, including bio-inspired, swarm-based, socio-cultural, and physics-based methods or algorithms; their testing and validation, along with detailed illustrative solutions and applications; and newly devised metaheuristic algorithms.
This will be a valuable reference for researchers in industry and academia, as well as for all Master's and PhD students working in the metaheuristics and applications domains.
Frequently asked questions
- Essential is ideal for learners and professionals who enjoy exploring a wide range of subjects. Access the Essential Library with 800,000+ trusted titles and best-sellers across business, personal growth, and the humanities. Includes unlimited reading time and Standard Read Aloud voice.
- Complete: Perfect for advanced learners and researchers needing full, unrestricted access. Unlock 1.4M+ books across hundreds of subjects, including academic and specialized titles. The Complete Plan also includes advanced features like Premium Read Aloud and Research Assistant.
Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Information
Table of contents
- Cover
- Half Title
- Series Page
- Title Page
- Copyright Page
- Dedication
- Contents
- Preface
- Editors
- List of Contributors
- SECTION I Bio-Inspired Methods
- SECTION II Physics and Chemistry-Based Methods
- SECTION III Socio-inspired Methods
- SECTION IV Swarm-Based Methods
- Index