Advanced Optimization Methods with Hybrid Metaheuristic and Intelligent Techniques : Hybrid Metaheuristic and Intelligent Techniques for Engineering Optimization

Manisha Sharma

ISBN 13: 9798181241282

Published by Independently Published Jun 2026, 2026

About this Item

Neuware.

Seller Inventory # 9798181241282

Bibliographic Details

• Title: Advanced Optimization Methods with Hybrid Metaheuristic and Intelligent Techniques : Hybrid Metaheuristic and Intelligent Techniques for Engineering Optimization
• Publisher: Independently Published Jun 2026
• Publication Date: 2026
• Language: English
• ISBN 13: 9798181241282
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 22D millimeters 229 millimeters width by 152 millimeters height by 22 millimeters depth
• Weight: 578 grams

About this title

Synopsis

Advanced Optimization Methods with Hybrid Metaheuristic and Intelligent Techniques

Optimization lies at the heart of modern engineering, artificial intelligence, data science, operations research, and industrial decision-making. Advanced Optimization Methods with Hybrid Metaheuristic and Intelligent Techniques provides a comprehensive and practical guide to both classical and modern optimization approaches used to solve complex real-world problems.

This book takes readers from fundamental optimization concepts to advanced hybrid intelligent algorithms. It covers mathematical modeling, linear and nonlinear programming, dynamic programming, convex optimization, evolutionary computation, swarm intelligence, and state-of-the-art hybrid optimization techniques. Special emphasis is placed on integrating artificial intelligence, machine learning, fuzzy systems, neural networks, and reinforcement learning with optimization frameworks.

Designed for undergraduate, postgraduate, and research scholars in engineering, computer science, data science, and operations research, the book combines rigorous theory with practical implementation. Readers will find numerous engineering examples, case studies, solved numerical problems, Python-based implementations, optimization workflows, review questions, programming exercises, and mini-projects that bridge the gap between theory and real-world applications.

Key Features

• Comprehensive coverage of classical, metaheuristic, and hybrid optimization methods.

• Detailed mathematical formulations and optimization theory.

• Python implementations using modern scientific computing libraries.

• Engineering-focused case studies and industrial applications.

• Multi-objective optimization and Pareto-based decision-making techniques.

• Integration of AI, machine learning, fuzzy logic, and intelligent systems.

• Practice problems, review questions, and project-based learning activities.

• Suitable for academic coursework, competitive examinations, research, and industrial practice.

Whether you are a student learning optimization for the first time, a researcher exploring advanced algorithms, or a professional seeking practical optimization solutions, this book provides the knowledge, tools, and techniques needed to tackle challenging optimization problems in modern engineering and intelligent systems.