 Название: Constraint Handling in Metaheuristics and Applications
Автор: Anand J. Kulkarni, Efren Mezura-Montes, Yong Wang, Ganesh Krishnasamy
Издательство: Springer
Год: 2021
Страниц: 336
Язык: английский
Формат: pdf (true), epub
Размер: 35.7 MB
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.
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.
Chapter “Nature-Inspired Metaheuristic Algorithms for Constraint Handling: Challenges, Issues, and Research Perspective” reviews the importance of metaheuristic algorithms, their classification, various constraints handling techniques, applications, etc. The applications reviewed are associated with healthcare, data clustering, power system problem, prediction, etc. The metaheuristics are listed with associated strong application domains considering no-free-lunch approach. The constraint handling techniques such as penalty-based methods have been reviewed associated with the listed metaheuristics.
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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