Multi-objective Evolutionary Algorithms and Their Applications

Author: Cui Xunxue
Publisher:
Publish Date: 2006-06-01
Features: Solving multi-objective optimization problems using evolutionary algorithms is a hot and important field in intelligent computing. It breaks through the limitations of classical operations research methods for multi-objective optimization and has distinct features compared to traditional single-objective evolutionary algorithms. It holds high application value in industrial engineering, science, and national defense and military. This book systematically and comprehensively introduces and discusses the fundamental knowledge and issues related to the theory and application of multi-objective evolutionary algorithms. The main contents include: the basic concepts of multi-objective optimization and evolutionary algorithms; major multi-objective evolutionary algorithms; theoretical issues of multi-objective evolutionary algorithms; theoretical issues of performance-based methods for designing new evolutionary algorithms for multi-objective optimization; performance evaluation and testing issues for designing new evolutionary algorithms for multi-objective optimization; typical application examples. Additionally, it focuses on introducing the theoretical methods and application examples of particle swarm optimization (PSO), a recently emerging algorithm in the field of evolutionary algorithms, for handling multi-objective problems. Based on references to domestic and international books and leveraging the research achievements of collaborators, this book presents in detail and comprehensively the research progress of multi-objective evolutionary algorithms, demonstrating novelty, academic rigor, and practical applicability. The content is presented in an accessible yet profound manner, reflecting the research development trends in this field and its applications in other disciplines. The book is accompanied by a rich list of references for self-study. It is suitable for teaching, research, and engineering application personnel in the fields of optimization and artificial intelligence. It can also serve as a teaching reference book for teachers, researchers, and senior university students in related disciplines such as computer science, automation, systems engineering, and military technology, or as a self-study reference book for researchers and engineering in other fields who wish to delve deeper into multi-objective evolutionary algorithms.