Diversity-Driven Evolutionary Algorithms For Solving Engineering Problems explores optimization algorithms and their applications across diverse engineering domains. It presents a comprehensive exploration of both classical and modern optimization techniques, emphasizing their role in solving complex, real-world problems. The book bridges theoretical foundations with practical implementation, providing readers with the knowledge to understand, analyze, and apply these algorithms effectively.
A core theme revolves around the development of a novel evolutionary algorithm, the Diversity-Driven Multi-Parent Evolutionary Algorithm with Adaptive Non-Uniform Mutation (DDMPEA-ANUM), with a detailed examination of its mechanics and performance characteristics. The book's scope extends across multiple engineering disciplines, showcasing the adaptability and power of optimization methods. Specific applications include the design of digital filters (both IIR and QMF banks), resource management in heterogeneous wireless sensor networks (HWSNs), and fault diagnosis in mechanical systems. Beyond the theoretical analysis and algorithm development, the book offers practical insights into the implementation and evaluation of optimization strategies. Real-world datasets and case studies are presented to illustrate the effectiveness of the proposed methods, demonstrating their potential for solving critical engineering challenges. The inclusion of statistical analysis, such as the Wilcoxon rank-sum test, ensures the robustness and reliability of the findings.
By blending theoretical depth with practical relevance, this book serves as a valuable resource for researchers, engineers, and graduate students seeking to master the art of optimization in a wide range of applications.