Comprehensive Metaheuristics. Algorithms and Applications
Par : ,Formats :
- Nombre de pages446
- PrésentationBroché
- FormatGrand Format
- Poids0.965 kg
- Dimensions19,0 cm × 23,5 cm × 2,0 cm
- ISBN978-0-323-91781-0
- EAN9780323917810
- Date de parution02/02/2023
- ÉditeurAcademic Press
Résumé
Metaheuristics are general-purpose problem-solving Artificial Intelligence (Al) techniques that can be used to solve any sort of optimization problems, subject to the proper configuration. Comprehensive Metaheuristics : Algorithms and Applications presents the foundational underpinnings of metaheuristics and the broad scope of algorithms and real-world applications across a variety of research fields.
The book begins by presenting fundamentals, mathematical prerequisites, and conceptual approaches to provide readers with a solid foundation of understanding. 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. Algorithms and techniques covered in Part 1 include Genetic Algorithm, Particle Swarm Optimization, Krill Herd Algorithm, Cuckoo Search Algorithm, Bat Algorithm, Grey Wolf Optimizer, Salp Swarm Optimizer, Dragonfly Algorithm, Grasshopper Optimization Algorithm, Whale Optimization Algorithm, Equilibrium Optimizer, Marine Predator Algorithm, Arithmetic Optimization Algorithm, and Differential Evolution.
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 presented in Part 2 of the book. This book takes a much-needed holistic approach, combining the most widely used metaheuristic algorithms with an in-depth treatise on multidisciplinary 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, algorithms, and techniques of metaheuristics. Key Features : World-renowned researchers and practitioners in metaheuristics present techniques,algorithms, and applications based on real-world case studies ; The book presents methodology for formulating optimization problems for metaheuristics ; The book teaches readers to analyze and tune the performance of a metaheuristic and integrare metaheuristics into other Al techniques ; All source code from the applications and algorithms is available online About the Editors Seyedali Mirjalili is a Professor at Torrens University Australia and the founding director of the Center for Artificial Intelligence Research and Optimization.
He has published more than 300 journal articles with an H-index of 80. He has been listed on the top 1% of highly cited researchers and named one of the most influential researchers in the world by Web of Science since 2019. In 2021, The Australian newspaper named him the top researcher in Australia in artificial intelligence, evolutionary computation, and fuzzy systems. Professor Mirjalili is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and an editor of leading Al joumals including Neurocomputlng, Applied Soh Computing, Advances in Engineering Software, Computers in Biology and Medicine, Healthcare Analytics, Applied Intelligence, and Decision Analytics.
His research interests are optimization, evolutionary computation, meta-heuristics, machine learning, and data science. Arnie H. Gandomi is Professor of Data Science and an Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) fellow at the Faculty of Engineering and Information Technology, University of Technology Sydney (UTS). Prior to joining UTS, Prof. Gandomi was an assistant professor at Stevens Institute of Technology, USA, and a distinguished research fellow at the BEACON Center for the Study of Evolution in Action, Michigan State University, United States.
Prof. Gandomi has published 330 journal papers and 12 books which collectively h ve been cited 33,000+ Omen (H-index e 82). He has been named as one of the most influential scientific minds and received the Highly Cited Researcher award (top 1% publications and 0.1%researchers) from Web of Science for six consecutiveyears,2017 to 2022. Healso ranked 17th in GP bib lographyamo gmore than 15,000 researchers.
He hàs received multiple prestigious awards for his research excellence and impact, such as the 2022 Walter L. Huber Prize, the highest level mid-career research award in all areas of civil engineering. His research interests are global optimisation and (big) data ana ytics using machine learning and evolutionary computations in particular.
The book begins by presenting fundamentals, mathematical prerequisites, and conceptual approaches to provide readers with a solid foundation of understanding. 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. Algorithms and techniques covered in Part 1 include Genetic Algorithm, Particle Swarm Optimization, Krill Herd Algorithm, Cuckoo Search Algorithm, Bat Algorithm, Grey Wolf Optimizer, Salp Swarm Optimizer, Dragonfly Algorithm, Grasshopper Optimization Algorithm, Whale Optimization Algorithm, Equilibrium Optimizer, Marine Predator Algorithm, Arithmetic Optimization Algorithm, and Differential Evolution.
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 presented in Part 2 of the book. This book takes a much-needed holistic approach, combining the most widely used metaheuristic algorithms with an in-depth treatise on multidisciplinary 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, algorithms, and techniques of metaheuristics. Key Features : World-renowned researchers and practitioners in metaheuristics present techniques,algorithms, and applications based on real-world case studies ; The book presents methodology for formulating optimization problems for metaheuristics ; The book teaches readers to analyze and tune the performance of a metaheuristic and integrare metaheuristics into other Al techniques ; All source code from the applications and algorithms is available online About the Editors Seyedali Mirjalili is a Professor at Torrens University Australia and the founding director of the Center for Artificial Intelligence Research and Optimization.
He has published more than 300 journal articles with an H-index of 80. He has been listed on the top 1% of highly cited researchers and named one of the most influential researchers in the world by Web of Science since 2019. In 2021, The Australian newspaper named him the top researcher in Australia in artificial intelligence, evolutionary computation, and fuzzy systems. Professor Mirjalili is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and an editor of leading Al joumals including Neurocomputlng, Applied Soh Computing, Advances in Engineering Software, Computers in Biology and Medicine, Healthcare Analytics, Applied Intelligence, and Decision Analytics.
His research interests are optimization, evolutionary computation, meta-heuristics, machine learning, and data science. Arnie H. Gandomi is Professor of Data Science and an Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) fellow at the Faculty of Engineering and Information Technology, University of Technology Sydney (UTS). Prior to joining UTS, Prof. Gandomi was an assistant professor at Stevens Institute of Technology, USA, and a distinguished research fellow at the BEACON Center for the Study of Evolution in Action, Michigan State University, United States.
Prof. Gandomi has published 330 journal papers and 12 books which collectively h ve been cited 33,000+ Omen (H-index e 82). He has been named as one of the most influential scientific minds and received the Highly Cited Researcher award (top 1% publications and 0.1%researchers) from Web of Science for six consecutiveyears,2017 to 2022. Healso ranked 17th in GP bib lographyamo gmore than 15,000 researchers.
He hàs received multiple prestigious awards for his research excellence and impact, such as the 2022 Walter L. Huber Prize, the highest level mid-career research award in all areas of civil engineering. His research interests are global optimisation and (big) data ana ytics using machine learning and evolutionary computations in particular.
Metaheuristics are general-purpose problem-solving Artificial Intelligence (Al) techniques that can be used to solve any sort of optimization problems, subject to the proper configuration. Comprehensive Metaheuristics : Algorithms and Applications presents the foundational underpinnings of metaheuristics and the broad scope of algorithms and real-world applications across a variety of research fields.
The book begins by presenting fundamentals, mathematical prerequisites, and conceptual approaches to provide readers with a solid foundation of understanding. 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. Algorithms and techniques covered in Part 1 include Genetic Algorithm, Particle Swarm Optimization, Krill Herd Algorithm, Cuckoo Search Algorithm, Bat Algorithm, Grey Wolf Optimizer, Salp Swarm Optimizer, Dragonfly Algorithm, Grasshopper Optimization Algorithm, Whale Optimization Algorithm, Equilibrium Optimizer, Marine Predator Algorithm, Arithmetic Optimization Algorithm, and Differential Evolution.
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 presented in Part 2 of the book. This book takes a much-needed holistic approach, combining the most widely used metaheuristic algorithms with an in-depth treatise on multidisciplinary 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, algorithms, and techniques of metaheuristics. Key Features : World-renowned researchers and practitioners in metaheuristics present techniques,algorithms, and applications based on real-world case studies ; The book presents methodology for formulating optimization problems for metaheuristics ; The book teaches readers to analyze and tune the performance of a metaheuristic and integrare metaheuristics into other Al techniques ; All source code from the applications and algorithms is available online About the Editors Seyedali Mirjalili is a Professor at Torrens University Australia and the founding director of the Center for Artificial Intelligence Research and Optimization.
He has published more than 300 journal articles with an H-index of 80. He has been listed on the top 1% of highly cited researchers and named one of the most influential researchers in the world by Web of Science since 2019. In 2021, The Australian newspaper named him the top researcher in Australia in artificial intelligence, evolutionary computation, and fuzzy systems. Professor Mirjalili is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and an editor of leading Al joumals including Neurocomputlng, Applied Soh Computing, Advances in Engineering Software, Computers in Biology and Medicine, Healthcare Analytics, Applied Intelligence, and Decision Analytics.
His research interests are optimization, evolutionary computation, meta-heuristics, machine learning, and data science. Arnie H. Gandomi is Professor of Data Science and an Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) fellow at the Faculty of Engineering and Information Technology, University of Technology Sydney (UTS). Prior to joining UTS, Prof. Gandomi was an assistant professor at Stevens Institute of Technology, USA, and a distinguished research fellow at the BEACON Center for the Study of Evolution in Action, Michigan State University, United States.
Prof. Gandomi has published 330 journal papers and 12 books which collectively h ve been cited 33,000+ Omen (H-index e 82). He has been named as one of the most influential scientific minds and received the Highly Cited Researcher award (top 1% publications and 0.1%researchers) from Web of Science for six consecutiveyears,2017 to 2022. Healso ranked 17th in GP bib lographyamo gmore than 15,000 researchers.
He hàs received multiple prestigious awards for his research excellence and impact, such as the 2022 Walter L. Huber Prize, the highest level mid-career research award in all areas of civil engineering. His research interests are global optimisation and (big) data ana ytics using machine learning and evolutionary computations in particular.
The book begins by presenting fundamentals, mathematical prerequisites, and conceptual approaches to provide readers with a solid foundation of understanding. 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. Algorithms and techniques covered in Part 1 include Genetic Algorithm, Particle Swarm Optimization, Krill Herd Algorithm, Cuckoo Search Algorithm, Bat Algorithm, Grey Wolf Optimizer, Salp Swarm Optimizer, Dragonfly Algorithm, Grasshopper Optimization Algorithm, Whale Optimization Algorithm, Equilibrium Optimizer, Marine Predator Algorithm, Arithmetic Optimization Algorithm, and Differential Evolution.
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 presented in Part 2 of the book. This book takes a much-needed holistic approach, combining the most widely used metaheuristic algorithms with an in-depth treatise on multidisciplinary 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, algorithms, and techniques of metaheuristics. Key Features : World-renowned researchers and practitioners in metaheuristics present techniques,algorithms, and applications based on real-world case studies ; The book presents methodology for formulating optimization problems for metaheuristics ; The book teaches readers to analyze and tune the performance of a metaheuristic and integrare metaheuristics into other Al techniques ; All source code from the applications and algorithms is available online About the Editors Seyedali Mirjalili is a Professor at Torrens University Australia and the founding director of the Center for Artificial Intelligence Research and Optimization.
He has published more than 300 journal articles with an H-index of 80. He has been listed on the top 1% of highly cited researchers and named one of the most influential researchers in the world by Web of Science since 2019. In 2021, The Australian newspaper named him the top researcher in Australia in artificial intelligence, evolutionary computation, and fuzzy systems. Professor Mirjalili is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and an editor of leading Al joumals including Neurocomputlng, Applied Soh Computing, Advances in Engineering Software, Computers in Biology and Medicine, Healthcare Analytics, Applied Intelligence, and Decision Analytics.
His research interests are optimization, evolutionary computation, meta-heuristics, machine learning, and data science. Arnie H. Gandomi is Professor of Data Science and an Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) fellow at the Faculty of Engineering and Information Technology, University of Technology Sydney (UTS). Prior to joining UTS, Prof. Gandomi was an assistant professor at Stevens Institute of Technology, USA, and a distinguished research fellow at the BEACON Center for the Study of Evolution in Action, Michigan State University, United States.
Prof. Gandomi has published 330 journal papers and 12 books which collectively h ve been cited 33,000+ Omen (H-index e 82). He has been named as one of the most influential scientific minds and received the Highly Cited Researcher award (top 1% publications and 0.1%researchers) from Web of Science for six consecutiveyears,2017 to 2022. Healso ranked 17th in GP bib lographyamo gmore than 15,000 researchers.
He hàs received multiple prestigious awards for his research excellence and impact, such as the 2022 Walter L. Huber Prize, the highest level mid-career research award in all areas of civil engineering. His research interests are global optimisation and (big) data ana ytics using machine learning and evolutionary computations in particular.