Computational Optimization Techniques: Algorithms and Applications

Rupesh Kumar Tipu

ISBN 10: 6207806778 / ISBN 13: 9786207806775

Published by LAP LAMBERT Academic Publishing Jun 2024, 2024

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -'Computational Optimization Techniques: Algorithms and Applications' provides a comprehensive exploration of optimization methods, ranging from classical techniques like linear and nonlinear programming to advanced metaheuristic algorithms such as genetic algorithms, particle swarm optimization, and ant colony optimization. The book delves into the mathematical foundations of optimization, practical implementations with detailed code examples, and various real-world applications, including engineering design, machine learning, and scheduling problems. Additionally, it highlights the benefits of hybrid metaheuristic methods, combining different algorithms to enhance performance and robustness. This book serves as a valuable resource for students, researchers, and practitioners seeking to understand and apply optimization techniques in diverse fields.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 76 pp. Englisch.

Seller Inventory # 9786207806775

Bibliographic Details

• Title: Computational Optimization Techniques: Algorithms and Applications
• Publisher: LAP LAMBERT Academic Publishing Jun 2024
• Publication Date: 2024
• Language: English
• ISBN 10: 6207806778
• ISBN 13: 9786207806775
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 131 grams

About this title

Synopsis

"Computational Optimization Techniques: Algorithms and Applications" provides a comprehensive exploration of optimization methods, ranging from classical techniques like linear and nonlinear programming to advanced metaheuristic algorithms such as genetic algorithms, particle swarm optimization, and ant colony optimization. The book delves into the mathematical foundations of optimization, practical implementations with detailed code examples, and various real-world applications, including engineering design, machine learning, and scheduling problems. Additionally, it highlights the benefits of hybrid metaheuristic methods, combining different algorithms to enhance performance and robustness. This book serves as a valuable resource for students, researchers, and practitioners seeking to understand and apply optimization techniques in diverse fields.