Handbook of Nature-Inspired Optimization Algorithms: The State of the Art : Volume II: Solving Constrained Single Objective Real-Parameter Optimization Problems

Ali Wagdy Mohamed

ISBN 10: 3031075153 / ISBN 13: 9783031075155

Published by Springer International Publishing, 2022

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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 graduateclass on optimization, but will also be useful for interested senior students working on their research projects.

Seller Inventory # 9783031075155

Bibliographic Details

• Title: Handbook of Nature-Inspired Optimization Algorithms: The State of the Art : Volume II: Solving Constrained Single Objective Real-Parameter Optimization Problems
• Publisher: Springer International Publishing
• Publication Date: 2022
• Language: English
• ISBN 10: 3031075153
• ISBN 13: 9783031075155
• Binding: Buch
• Condition: Neu

About this title

Synopsis

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 graduateclass on optimization, but will also be useful for interested senior students working on their research projects.