Constraint Handling in Metaheuristics and Applications
Editat de Anand J. Kulkarni, Efrén Mezura-Montes, Yong Wang, Amir H. Gandomi, Ganesh Krishnasamyen Limba Engleză Paperback – 13 apr 2022
The book also provides critical review of the contemporary constraint handling approaches. The contributions of the book may further help to explore new avenues leading towards multidisciplinary research discussions. This book is a complete reference for engineers, scientists, and students studying/working in the optimization, artificial intelligence (AI), or computational intelligence arena.
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Specificații
ISBN-13: 9789813367128
ISBN-10: 9813367121
Pagini: 315
Ilustrații: XXIX, 315 p. 79 illus., 64 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.49 kg
Ediția:1st ed. 2021
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore
ISBN-10: 9813367121
Pagini: 315
Ilustrații: XXIX, 315 p. 79 illus., 64 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.49 kg
Ediția:1st ed. 2021
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore
Cuprins
Chapter 1. An improved Cohort Intelligence with Panoptic Learning Behavior for solving constrained problems.- Chapter 2. Online Landscape Analysis for Guiding Constraint Handling in Particle Swarm Optimisation.- Chapter 3. Constrained Array Synthesis Techniques using novel metaheuristics. Chapter 3. An Algorithm to Select a Fractional Order Model Using Metaheuristic Approach.- Chapter 5. Implementation of Fractional PID Controller for Surface Roughness Generation in Machining Nanocomposites using Cohort Intelligence.- Chapter 6. Recent Advances in Constraint Handling of Mechanism Synthesis.- Chapter 7. Application of Metaheuristic Algorithms for Feature Selection.- Chapter 8. Different Constraint Handling Methods Affections on The Performance of Particle Swarm Optimization Algorithm.
Notă biografică
Anand J Kulkarni holds a PhD in Distributed Optimization from Nanyang Technological University, Singapore, MS in Artificial Intelligence from University of Regina, Canada, Bachelor of Engineering from Shivaji University, India and Diploma from the Board of Technical Education, Mumbai. He worked as a Research Fellow on a Cross-border Supply-chain Disruption project at Odette School of Business, University of Windsor, Canada. Anand was Chair of the Mechanical Engineering Department at Symbiosis International (Deemed University) (SIU), Pune, India for three years. Currently, he is Associate Professor at the Symbiosis Center for Research and Innovation, SIU. His research interests include optimization algorithms, multi-objective optimization, continuous, discrete and combinatorial optimization, multi-agent systems, complex systems, probability collectives, swarm optimization, game theory, self-organizing systems and fault-tolerant systems. Anand pioneered socio-inspired optimization methodologies such as Cohort Intelligence, Ideology Algorithm, Expectation Algorithm, Socio Evolution & Learning Optimization algorithm. He is the founder and chairman of the Optimization and Agent Technology (OAT) Research Lab and has published over 60 research papers in peer-reviewed journals, chapters and conferences along with 4 authored and 5 edited books.
Dr Efrén Mezura-Montes is a full-time researcher with the Artificial Intelligence Research Center at the University of Veracruz, MEXICO. His research interests are the design, analysis and application of bio-inspired algorithms to solve complex optimization problems. He has published over 150 papers in peer-reviewed journals and conferences. He also has one edited book and over 11 book chapters published by international publishing companies. From his work, Google Scholar reports over 6,900 citations. Dr Mezura-Montes is a member of the editorial board of the journals: “Swarm and Evolutionary Computation”, “Complex & Intelligent Systems”, “International Journal of Dynamics and Control”, the “Journal of Optimization” and the “International Journal of Students' Research in Technology & Management”. He is a former member of the editorial board of the journals “Computational Optimization and Applications” and “Soft Computing”. He is also a reviewer for more than 20 international specialized journals, including the MIT Press Evolutionary Computation Journal, IEEE Transactions on Evolutionary Computation and the IEEE Transactions on Cybernetics. Dr Mezura-Montes is a member of the IEEE Computational Intelligence Society Evolutionary Computation Technical Committee and he is the founder of the IEEE Computational Intelligence Society task force on Nature-Inspired Constrained Optimization. He is a member of the IEEE Systems Man and Cybernetics Society Soft Computing Technical Committee. He is also regular member of the Machine Intelligence Research Labs (MIR Labs). Dr Mezura-Montes is a Level-2 member of the Mexican National Researchers System (SNI). Moreover, Dr Mezura-Montes is a regular member of the Mexican Academy of Sciences (AMC), a regular member of the Mexican Computing Academy (AMEXCOMP) and also a member of Technical Committee of the Mexican Science Council (CONACyT) Collaboration network on Applied Computational Intelligence.
Amir H Gandomi is a Professor of Data Science at the Faculty of Engineering & Information Technology, University of Technology Sydney. Prior to joining UTS, Prof. Gandomi was an Assistant Professor at the School of Business, Stevens Institute of Technology, USA and a distinguished research fellow in BEACON center, Michigan State University, USA. Prof. Gandomi has published over one hundred and ninety journal papers and seven books which collectively have been cited more than 16,000 times (H-index = 59). He has been named as one of the most influential scientific mind and Highly Cited Researcher (top 1%) for three consecutive years, 2017 to 2019. 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. Prof Gandomi is active in delivering keynotes and invited talks. His research interests are global optimization and (big) data analytics using machine learning and evolutionary computations in particular.Yong Wang received the Ph.D. degree in control science and engineering from the Central South University, Changsha, China, in 2011. He is a Professor with the School of Automation, Central South University, Changsha, China. His current research interests include the theory, algorithm design, and interdisciplinary applications of computational intelligence. Dr. Wang is an Associate Editor for the IEEE Transactions on Evolutionary Computation and the Swarm and Evolutionary Computation. He was a recipient of Cheung Kong Young Scholar by the Ministry of Education, China, in 2018, and a Web of Science highly cited researcher in Computer Science in 2017 and 2018. Ganesh Krishnasamy joined the School of Information Technology, Monash University Malaysia as a Lecturer in July 2019. He received the B.Eng. and M.Eng. degrees in electrical and electronic engineering from Universiti Kebangsaan Malaysia in 2004 and 2007. After working in the manufacturing industry for more than 4 years, he continued his doctoral studies at the University of Malaya where he completed his Ph.D. degree in Electrical Engineering. After obtaining his Ph.D., he worked at Sime Darby Plantation as a data scientist for about a year. His current research interests include the field of computer vision, machine learning, and optimization.
Dr Efrén Mezura-Montes is a full-time researcher with the Artificial Intelligence Research Center at the University of Veracruz, MEXICO. His research interests are the design, analysis and application of bio-inspired algorithms to solve complex optimization problems. He has published over 150 papers in peer-reviewed journals and conferences. He also has one edited book and over 11 book chapters published by international publishing companies. From his work, Google Scholar reports over 6,900 citations. Dr Mezura-Montes is a member of the editorial board of the journals: “Swarm and Evolutionary Computation”, “Complex & Intelligent Systems”, “International Journal of Dynamics and Control”, the “Journal of Optimization” and the “International Journal of Students' Research in Technology & Management”. He is a former member of the editorial board of the journals “Computational Optimization and Applications” and “Soft Computing”. He is also a reviewer for more than 20 international specialized journals, including the MIT Press Evolutionary Computation Journal, IEEE Transactions on Evolutionary Computation and the IEEE Transactions on Cybernetics. Dr Mezura-Montes is a member of the IEEE Computational Intelligence Society Evolutionary Computation Technical Committee and he is the founder of the IEEE Computational Intelligence Society task force on Nature-Inspired Constrained Optimization. He is a member of the IEEE Systems Man and Cybernetics Society Soft Computing Technical Committee. He is also regular member of the Machine Intelligence Research Labs (MIR Labs). Dr Mezura-Montes is a Level-2 member of the Mexican National Researchers System (SNI). Moreover, Dr Mezura-Montes is a regular member of the Mexican Academy of Sciences (AMC), a regular member of the Mexican Computing Academy (AMEXCOMP) and also a member of Technical Committee of the Mexican Science Council (CONACyT) Collaboration network on Applied Computational Intelligence.
Amir H Gandomi is a Professor of Data Science at the Faculty of Engineering & Information Technology, University of Technology Sydney. Prior to joining UTS, Prof. Gandomi was an Assistant Professor at the School of Business, Stevens Institute of Technology, USA and a distinguished research fellow in BEACON center, Michigan State University, USA. Prof. Gandomi has published over one hundred and ninety journal papers and seven books which collectively have been cited more than 16,000 times (H-index = 59). He has been named as one of the most influential scientific mind and Highly Cited Researcher (top 1%) for three consecutive years, 2017 to 2019. 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. Prof Gandomi is active in delivering keynotes and invited talks. His research interests are global optimization and (big) data analytics using machine learning and evolutionary computations in particular.Yong Wang received the Ph.D. degree in control science and engineering from the Central South University, Changsha, China, in 2011. He is a Professor with the School of Automation, Central South University, Changsha, China. His current research interests include the theory, algorithm design, and interdisciplinary applications of computational intelligence. Dr. Wang is an Associate Editor for the IEEE Transactions on Evolutionary Computation and the Swarm and Evolutionary Computation. He was a recipient of Cheung Kong Young Scholar by the Ministry of Education, China, in 2018, and a Web of Science highly cited researcher in Computer Science in 2017 and 2018. Ganesh Krishnasamy joined the School of Information Technology, Monash University Malaysia as a Lecturer in July 2019. He received the B.Eng. and M.Eng. degrees in electrical and electronic engineering from Universiti Kebangsaan Malaysia in 2004 and 2007. After working in the manufacturing industry for more than 4 years, he continued his doctoral studies at the University of Malaya where he completed his Ph.D. degree in Electrical Engineering. After obtaining his Ph.D., he worked at Sime Darby Plantation as a data scientist for about a year. His current research interests include the field of computer vision, machine learning, and optimization.
Textul de pe ultima copertă
This book aims to discuss the core and underlying principles and analysis of the different constraint handling approaches. The main emphasis of the book is on providing an enriched literature on mathematical modelling of the test as well as real-world problems with constraints, and further development of generalized constraint handling techniques. These techniques may be incorporated in suitable metaheuristics providing a solid optimized solution to the problems and applications being addressed. The book comprises original contributions with an aim to develop and discuss generalized constraint handling approaches/techniques for the metaheuristics and/or the applications being addressed. A variety of novel as well as modified and hybridized techniques have been discussed in the book. The conceptual as well as the mathematical level in all the chapters is well within the grasp of the scientists as well as the undergraduate and graduate students from the engineering and computer science streams. The reader is encouraged to have basic knowledge of probability and mathematical analysis and optimization.
The book also provides critical review of the contemporary constraint handling approaches. The contributions of the book may further help to explore new avenues leading towards multidisciplinary research discussions. This book is a complete reference for engineers, scientists, and students studying/working in the optimization, artificial intelligence (AI), or computational intelligence arena.
The book also provides critical review of the contemporary constraint handling approaches. The contributions of the book may further help to explore new avenues leading towards multidisciplinary research discussions. This book is a complete reference for engineers, scientists, and students studying/working in the optimization, artificial intelligence (AI), or computational intelligence arena.
Caracteristici
Discusses core and underlying mechanisms of different constraint handling approaches
Provides a platform for validation of novel constraint handling approaches and a complete reference to the in-depth inter-comparative analysis of existing constraint handling approaches
Intends to provide a platform for newly developed real-world constrained problems and their state-of-the-art solutions using AI-based metaheuristics
Serves as a platform to explore and exploit the inbuilt characteristics of algorithms for handling constraints
Provides a platform for validation of novel constraint handling approaches and a complete reference to the in-depth inter-comparative analysis of existing constraint handling approaches
Intends to provide a platform for newly developed real-world constrained problems and their state-of-the-art solutions using AI-based metaheuristics
Serves as a platform to explore and exploit the inbuilt characteristics of algorithms for handling constraints