Handbook of Nature-Inspired Optimization Algorithms: The State of the Art (eBook, PDF)

Volume II: Solving Constrained Single Objective Real-Parameter Optimization Problems
Redaktion: Mohamed, Ali Wagdy; Suganthan, Ponnuthurai Nagaratnam; Oliva, Diego

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 214
• Erscheinungstermin: 3. September 2022
• Englisch
• ISBN-13: 9783031075162
• Artikelnr.: 65618340

Inhaltsangabe

Particle swarm optimization based optimization for in-dustry inspection.- Ant Algorithms: from Drawback Identification to Quality and Speed Improvement.- Fault location techniques based on traveling waves with application in the protection of distribution systems with renewable energy and particle swarm optimization.- Improved Particle Swarm Optimization and Non-Quadratic Penalty Method for Non-Linear Programming Problems with Equality Constraints.- Recent Trends in Face Recognition Using Metaheuristic Optimization.