Comprehensive Metaheuristics Algorithms and Applications 1st Edition
Author(s): Seyedali Mirjalili, Amir Gandomi
Publisher: Academic Press
ISBN: 9780323917810
Edition: 1st Edition
$39,99
Delivery: This can be downloaded Immediately after purchasing.
Version: Only PDF Version.
Compatible Devices: Can be read on any device (Kindle, NOOK, Android/IOS devices, Windows, MAC)
Quality: High Quality. No missing contents. Printable
Recommended Software: Check here
Important: No Access Code
Description
Description
Comprehensive Metaheuristics: Algorithms and Applications presents the foundational underpinnings of metaheuristics and a broad scope of algorithms and real-world applications across a variety of research fields. The book starts with fundamentals, mathematical prerequisites, and conceptual approaches to provide readers with a solid foundation. After presenting multi-objective optimization, constrained optimization, and problem formation for metaheuristics, world-renowned authors give readers in-depth understanding of the full spectrum of algorithms and techniques. Scientists, researchers, academicians, and practitioners who are interested in optimizing a process or procedure to achieve a goal will benefit from the case studies of real-world applications from different domains.
The book takes a much-needed holistic approach, putting the most widely used metaheuristic algorithms together with an in-depth treatise on multi-disciplinary applications of metaheuristics. Each algorithm is thoroughly analyzed to observe its behavior, providing a detailed tutorial on how to solve problems using metaheuristics. New case studies and research problem statements are also discussed, which will help researchers in their application of the concepts.
- Presented by world-renowned researchers and practitioners in metaheuristics
- Includes techniques, algorithms, and applications based on real-world case studies
- Presents the methodology for formulating optimization problems for metaheuristics
- Provides readers with methods for analyzing and tuning the performance of a metaheuristic, as well as for integrating metaheuristics in other AI techniques
- Features online complementary source code from the applications and algorithms
