Textbook in PDF format

Optimization methods have become increasingly crucial across various domains, serving as indispensable tools for addressing real-world challenges. These algorithms continuously evolve to produce high-quality solutions in a reasonable timeframe. Metaheuristic methods, inspired by the behavior of populations or swarms in nature, are particularly valuable for solving complex optimization problems where exact methods fall short. Mathematical optimization, encompassing diverse methods and techniques, has garnered significant attention in mathematics, computer science, and operations research due to its relevance in numerous practical applications, such as business, management, and engineering. While obtaining exact solutions for optimization problems may prove arduous or unfeasible, the integration of non-traditional algorithms, such as population-based optimization with local search strategies and individual learning procedures, offers a flexible and successful approach to finding high-quality solutions. These algorithms have demonstrated strong performance across various benchmark problems and real-world applications. However, evaluating their effectiveness demands careful consideration of algorithm design and analysis, as performance comparisons based solely on extensive experimental tests may not fully reveal their strengths and weaknesses. Moreover, some studies have highlighted that algorithm performance can be influenced by the specific characteristics of the test problems. Applied Mathematics and Optimization Techniques is an insightful and comprehensive book that delves into novel mathematical optimization techniques and their wide-ranging applications. The book provides valuable insights into cutting-edge optimization methods and their practical implementations, making it an indispensable resource for researchers, practitioners, and students alike. Covering various domains and real-world scenarios, the book offers a deep understanding of how mathematical optimization can be effectively applied to address complex challenges in business, engineering, and beyond