Handbook of Nature-inspired Optimization Algorithms - the State of the Art: Solving Constrained Single Objective Real-parameter Optimization Problems: Vol 2 da Mohamed, Ali (Editor)/ Oliva, Diego (Editor)/ Suganthan, Ponnuthurai Nagaratnam (Editor): Brand New Hardcover (2022)

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Titolo: Handbook of Nature-inspired Optimization Algorithms - the State of the Art: Solving Constrained Single Objective Real-parameter Optimization Problems: Vol 2
Editore: Springer Nature
Data di pubblicazione: 2022
Lingua: Inglese
ISBN 10: 3031075153
ISBN 13: 9783031075155
Rilegatura: Hardcover
Condizione: Brand New

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Riassunto

This book presents recent contributions and significant development, advanced issues, and challenges. In real-world problems and applications, most of the optimization problems involve different types of constraints. These problems are called constrained optimization problems (COPs). The optimization of the constrained optimization problems is considered a challenging task since the optimum solution(s) must be feasible. In their original design, evolutionary algorithms (EAs) are able to solve unconstrained optimization problems effectively. As a result, in the past decade, many researchers have developed a variety of constraint handling techniques, incorporated into (EAs) designs, to counter this deficiency.

The main objective for this book is to make available a self-contained collection of modern research addressing the general constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects.

Dalla quarta di copertina

This book presents recent contributions and significant development, advanced issues, and challenges. In real-world problems and applications, most of the optimization problems involve different types of constraints. These problems are called constrained optimization problems (COPs). The optimization of the constrained optimization problems is considered a challenging task since the optimum solution(s) must be feasible. In their original design, evolutionary algorithms (EAs) are able to solve unconstrained optimization problems effectively. As a result, in the past decade, many researchers have developed a variety of constraint handling techniques, incorporated into (EAs) designs, to counter this deficiency.

The main objective for this book is to make available a self-contained collection of modern research addressing the general constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects.

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