Comprehensive Metaheuristics
Algorithms and Applications
- 1st Edition - January 31, 2023
- Editors: Ali Mirjalili, Amir Hossein Gandomi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 1 7 8 1 - 0
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 7 2 6 7 - 3
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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Request a sales quoteComprehensive 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
Researchers, developers, and industry professionals in Information Technology and Computer Science, such as developers of AI, Machine Learning, and Deep Learning, as well as other research fields, including Biomedical
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Chapter 1: Chaos theory in metaheuristics
- Abstract
- 1: Introduction
- 2: Chaos system and chaotic maps
- 3: Chaotic strategies in metaheuristic optimization
- 4: An application with chaotic system
- References
- Chapter 2: Metaheuristic approaches for solving multiobjective optimization problems
- Abstract
- 1: Introduction
- 2: Related works
- 3: An overview of electric fish optimization
- 4: Multiobjective electric fish optimization algorithm
- 5: Experiments
- 6: Conclusion
- References
- Chapter 3: A brief overview of physics-inspired metaheuristics
- Abstract
- 1: Introduction
- 2: Classical mechanics-based metaheuristics
- 3: Fluid mechanics-based metaheuristics
- 4: Thermodynamics-based metaheuristics
- 5: Electromagnetism-based metaheuristics
- 6: Optics-based metaheuristics
- 7: Other physics-based metaheuristic algorithms
- 8: Conclusion
- References
- Chapter 4: Evolutionary computation techniques for optimal response actions against water distribution networks contamination
- Abstract
- 1: Introduction
- 2: Evolutionary computation
- 3: Methodology development and applications
- 4: Conclusion
- References
- Chapter 5: Metaheuristic technique for solving fuzzy nonlinear equations
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Preliminaries
- 3: A brief description of the GBO algorithm
- 4: Numerical examples
- 5: Conclusion
- References
- Chapter 6: Metaheuristic algorithms in network intrusion detection
- Abstract
- 1: Introduction
- 2: Metaheuristic algorithms
- 3: Methodology
- 4: Metaheuristic algorithms in IDS
- 5: Challenges and future direction
- 6: Conclusion
- References
- Chapter 7: Metaheuristic algorithms in text clustering
- Abstract
- 1: Introduction
- 2: Text clustering formulation procedure
- 3: Metaheuristic algorithms application in text clustering
- 4: Conclusion and possible future research focus
- References
- Chapter 8: Application of metaheuristic algorithms in optimal design of sewer collection systems
- Abstract
- 1: Introduction
- 2: Sewer collection systems
- 3: Metaheuristic algorithms
- 4: Applications
- 5: Summary and conclusion
- References
- Chapter 9: Space truss structures’ optimization using metaheuristic optimization algorithms
- Abstract
- 1: Introduction
- 2: African vulture optimization algorithm, artificial gorilla troops optimizer, and artificial hummingbird algorithms
- 3: Structural design optimization
- 4: Conclusion
- References
- Chapter 10: Metaheuristics for solving the wind turbine placement problem
- Abstract
- 1: Introduction
- 2: Artificial gorilla troops optimizers
- 3: Binary variants of artificial gorilla troops optimizers
- 4: Experimental setup
- 5: Results and discussion
- 6: Conclusion
- References
- Chapter 11: Use of metaheuristics in industrial development and their future perspectives
- Abstract
- 1: Introduction
- 2: Classification of metaheuristics
- 3: Optimization in industry
- 4: Future perspective of metaheuristics in industrial development
- 5: Conclusion
- References
- Chapter 12: Lévy flight and Chaos theory based metaheuristics for grayscale image thresholding
- Abstract
- Conflict of interest
- 1: Introduction
- 2: Literature survey
- 3: Gravitational search algorithm
- 4: Lévy flight and chaos theory-based gravitational search algorithm
- 5: Image segmentation using LCGSA technique
- 6: Experimental results and discussion
- 7: Conclusion and future scope
- References
- Chapter 13: Metaheuristics for optimal feature selection in high-dimensional datasets
- Abstract
- 1: Introduction
- 2: Characteristics of high-dimensional data
- 3: Feature selection in high-dimensional datasets
- 4: Metaheuristic approaches
- 5: Practical evaluation
- 6: Conclusion
- References
- Chapter 14: Optimal deployment of sensors for leakage detection in water distribution systems using metaheuristics
- Abstract
- 1: Introduction
- 2: Background
- 3: Related works
- 4: Methods
- 5: Objective functions
- 6: Computational experiments
- 7: Conclusions
- References
- Chapter 15: Metaheuristic-based automatic generation controller in interconnected power systems with renewable energy sources
- Abstract
- 1: Introduction
- 2: Renewable energy sources integrated power systems
- 3: The proposed PID-(1 + I) controller
- 4: Metaheuristic optimization techniques
- 5: Simulation results and discussions
- 6: Conclusion
- References
- Chapter 16: Route optimization in MANET using swarm intelligence algorithm
- Abstract
- 1: Introduction
- 2: Related work
- 3: Workflow of MANETs
- 4: Routing challenges in MANETs
- 5: Routing issues resolved by optimization
- 6: Comparative analysis
- 7: Conclusion
- References
- Chapter 17: The promise of metaheuristic algorithms for efficient operation of a highly complex power system
- Abstract
- 1: Introduction
- 2: Reptile search algorithm
- 3: Problem statement
- 4: Case study
- 5: Conclusions
- References
- Chapter 18: Genome sequence assembly using metaheuristics
- Abstract
- 1: Introduction
- 2: Past works
- 3: Genome sequencing
- 4: Combinatorial optimization
- 5: Experiments
- 6: Conclusions
- References
- Chapter 19: Metaheuristics for optimizing weights in neural networks
- Abstract
- 1: Introduction
- 2: Feedforward neural networks
- 3: Proposed algorithm
- 4: Experiments and results
- 5: Conclusion and future work
- References
- Chapter 20: Metaheuristics for clustering problems
- Abstract
- 1: Introduction
- 2: Data clustering problem
- 3: Data clustering using metaheuristic algorithms
- 4: Results and discussion
- 5: Conclusion and future works
- References
- Chapter 21: Employment of bio-inspired algorithms in the field of antenna array optimization: A review
- Abstract
- 1: Introduction
- 2: Flower pollination algorithm
- 3: Cat Swarm Optimization
- 4: Gravitational Search Algorithm
- 5: Case study
- 6: Conclusion
- References
- Chapter 22: Foundations of combinatorial optimization, heuristics, and metaheuristics
- Abstract
- 1: Introduction
- 2: Combinatorial optimization problems
- 3: Analysis of algorithms
- 4: Complexity of algorithms
- 5: Modeling a CO problem
- 6: Solution methods
- 7: Conclusion
- References
- Index
- No. of pages: 466
- Language: English
- Edition: 1
- Published: January 31, 2023
- Imprint: Academic Press
- Paperback ISBN: 9780323917810
- eBook ISBN: 9780323972673
AM
Ali Mirjalili
Senior lecturer S. Ali Mirjalili MD, PhD, PGDipSurgAnat, PGCertCPU works in the Anatomy and Medical Imaging Department and Convenor of Science for Surgens Course, Department of Surgery at University of Auckland, Malaysia.
Affiliations and expertise
Center for Artificial Intelligence Research and Optimization, Torrens University Australia University Research and Innovation Center, Obuda University, 1034 Budapest, HungaryAG
Amir Hossein Gandomi
Dr. Amir H. Gandomi is anARC DECRA Fellow at the Faculty of Engineering andInformation Technology, University of Technology Sydney, Australia. Prior to joining UTS, Dr. Gandomi was an Assistant Professor at Stevens Institute of Technology, USA and a Distinguished Research Fellow in BEACON center, Michigan State University, USA. Dr. Gandomi has published over two hundred journal papers and seven books which collectively have been cited 19,000+ times. He has been named as one of the most influential scientific mindsand Highly Cited Researcher (top 1% publications and 0.1% researchers) for four consecutive years, 2017 to 2020. He also ranked 18th in GP bibliography among more than 12,000 researchers. He has served as associate editor, editor and guest editor in several prestigious journals such as AE of SWEVO, IEEE TBD, and IEEE IoTJ. Dr. Gandomi is active in delivering keynotes and invited talks. His research interests are global optimization andbigdata analytics using Machine Learning and evolutionary computations in particular.
Affiliations and expertise
University of Technology Sydney, AustraliaRead Comprehensive Metaheuristics on ScienceDirect