"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.