Comprehensive Metaheuristics
Algorithms and Applications
- 1st Edition - January 31, 2023
- Latest edition
- Editors: Ali Mirjalili, Amir Hossein Gandomi
- Language: English
Comprehensive Metaheuristics: Algorithms and Applications presents the foundational underpinnings of metaheuristics and a broad scope of algorithms and real-world applicati… Read more
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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.
Key features
Key features
- 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
Readership
Readership
Table of contents
Table of contents
1. Chaos theory in metaheuristcs
2. A New Metaheuristic Approach for Solving Multi-Objective Optimization Problems
3. A Brief Overview of Physics-inspired Metaheuristic Optimization Techniques
4. Application of evolutionary computation techniques for developing optimal response actions against water distribution networks contamination
5. Metaheuristic Technique for Solving Fuzzy Nonlinear Equations
6. Meta-heuristics in Network Intrusion Detection
7. Meta-heuristics in text clustring
8. Application of Metaheuristic Algorithms for Optimal Design of Sewer Collection Systems
9. Meta-heuristics for optimization of structures
10. Meta-heuristics for solving wind turbine placement problem
11. Meta-heuristics in industrial development and its future perspective
12. Levy Flight and Chaos Theory Based Gravitational Search Algorithm for Greyscale Image Thresholding
13. Meta-heuristics for Optimal Feature Selection in High-Dimensional Datasets
14. The Optimal Deployment of Sensors for Leakage Detection in Water Distribution Systems using meta-heuristics
15. Meta-Heuristics Based Automatic Generation Controller In Interconnected Power Systems With Renewable Energy Sources
16. Swarm Intelligence based Route optimization in Mobile Ad-hoc Network
17. The promise of metaheuristic algorithms for efficient operation of a highly complex power system: a comparative assessment
18. Genome Sequencing: Applications of Meta-Heuristics in Bioinformatics
19. Meta-heuristics for training Neural Networks
20. Meta-heuristics for clustring problems
21. Bio Inspired Algorithms in the field of Antenna Array Optimization: Review
22. Foundations of Combinatorial Optimization, Heuristics, and Meta-heuristics
Product details
Product details
- Edition: 1
- Latest edition
- Published: February 2, 2023
- Language: English
About the editors
About the editors
AM
Ali Mirjalili
AG
Amir Hossein Gandomi
Amir H. Gandomi, PhD, is a leading researcher in global optimization and big data analytics, currently serving as a Professor of Data Science and an ARC DECRA Fellow at the University of Technology Sydney (UTS). With over 450 journal publications and 60,000 citations, he is among the most cited researchers worldwide. Dr. Gandomi has authored 14 books and received numerous accolades, including the IEEE TCSC Award and the Achenbach Medal. His editorial roles span several prestigious journals, and he is a sought-after keynote speaker in the fields of artificial intelligence and genetic programming. Previously, he held academic positions at the Stevens Institute of Technology and Michigan State University, where he contributed significantly to advancing knowledge in machine learning and evolutionary computation.