Parallel and Distributed Evolutionary Computation

Erscheinungsjahr 2027
ISBN: 978-981-9289-24-0
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

This book provides a comprehensive and systematic introduction to the deep integration of evolutionary computation and parallel distributed computing. It analyzes how parallel and distributed architectures can overcome the bottlenecks and challenges faced by evolutionary algorithms in solving large-scale and distributed complex optimization problems, and constructs a new paradigm of distributed evolutionary computation that is controllable, scalable, and trustworthy. By combining the inherent parallelism of evolutionary computation with modern parallel and distributed computing environments, the book innovatively categorizes parallel distributed evolutionary computation into two core dimensions: (1) Parallel acceleration for centralized optimization, such as GPU acceleration and cluster-based parallelism; (2) Collaborative optimization for distributed scenarios, such as multi-agent cooperation. The book covers key technologies including population-level parallelism and dimension-level parallelism. It defines three major classes of optimization problems—dimension-distributed, data-distributed, and objective-distributed optimization—and provides in-depth discussions on cutting-edge challenges such as data privacy and security. It offers readers a comprehensive theoretical framework as well as practical guidance. This book can serve as a textbook for graduate students and senior undergraduates, and as a reference for researchers. It aims to help readers gain a deep understanding of the core principles, technical details, and application prospects of parallel distributed evolutionary computation, thereby supporting further research and innovation in evolutionary computation and distributed computing.

Zielgruppe

Research

Autoren/Hrsg.

Chen, Wei-Neng

Wei, Feng-Feng

Geng, Ming-Can

Chen, Tai-You

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Netzwerk-Hardware
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Numerische Mathematik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme

Inhaltsverzeichnis

Chapter 1. Fundamentals of Evolutionary Computation.- Chapter 2. Evolutionary Computation Methods for Solving Complex Optimization Problems.- Chapter 3. Foundations of Parallel and Distributed Evolutionary Computation.- Chapter 4. Distributed Evolutionary Computation Methods for Acceleration.- Chapter 5. Distributed Evolutionary Computation Methods for Multi-Agent Collaboration.- Chapter 6. Distributed Evolutionary Computation Methods for Data Security and Privacy Protection.

Über Autor(innen)

Wei-Neng Chen received the bachelor’s and Ph.D. degrees in computer science from Sun Yat-sen University, Guangzhou, China, in 2006 and 2012, respectively. 
He is currently a Full Professor with the School of Computer Science and Engineering, South China University of Technology, Guangzhou. He has co-authored more than 200 international journal and conference papers, including more than 80 papers published in the IEEE Transactions journals. His current research interests include computational intelligence, swarm intelligence, network science, and their applications. He is currently the Vice-Chair of the IEEE Guangzhou Section, and the Chair of IEEE SMC Society Guangzhou Chapter. He is also a Committee Member of the IEEE CIS Emerging Topics Task Force. He was an Associate Editor for IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, and the Complex & Intelligent Systems.

Feng-Feng Wei received the bachelors and Ph.D. degrees in computer science and technology from the South China University of Technology, Guangzhou, China, in 2019 and 2023, respectively. She is currently an Associate Processor with the School of Computer Science and Engineering, South China University of Technology. Her current research interests include swarm intelligence, evolutionary computation, distributed optimization, multiagent systems, and their applications in real-world problems.

Ming-Can Geng received the bachelor’s degree in computer science and technology from the South China University of Technology, Guangzhou, China, in 2021, where he is currently pursuing the Ph.D. degree in computer science and technology with the School of Computer Science and Engineering. His current research interests include swarm intelligence, combinatorial optimization problems, and task allocation in computing systems.

Tai-You Chen received the Ph.D. degree in computer science from South China University of Technology in 2026. His research spans multi-agent systems, distributed and consensus-based optimization, and evolutionary computation, with seven first-author papers in JCR Q1 journals and top-tier conferences.