Extremal Optimization (inbunden)
Inbunden (Hardback)
Antal sidor
Productivity Press
Cheng, Peng (förf)
Four page color insert included. Eight color figures follows page 212; 193 lines of equations, 896 t
234 x 157 x 23 mm
636 g
Antal komponenter
Extremal Optimization (inbunden)

Extremal Optimization

Fundamentals, Algorithms, and Applications

Inbunden Engelska, 2016-03-04
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Extremal Optimization: Fundamentals, Algorithms, and Applications introduces state-of-the-art extremal optimization (EO) and modified EO (MEO) solutions from fundamentals, methodologies, and algorithms to applications based on numerous classic publications and the authors' recent original research results. It promotes the movement of EO from academic study to practical applications. The book covers four aspects, beginning with a general review of real-world optimization problems and popular solutions with a focus on computational complexity, such as "NP-hard" and the "phase transitions" occurring on the search landscape. Next, it introduces computational extremal dynamics and its applications in EO from principles, mechanisms, and algorithms to the experiments on some benchmark problems such as TSP, spin glass, Max-SAT (maximum satisfiability), and graph partition. It then presents studies on the fundamental features of search dynamics and mechanisms in EO with a focus on self-organized optimization, evolutionary probability distribution, and structure features (e.g., backbones), which are based on the authors' recent research results. Finally, it discusses applications of EO and MEO in multiobjective optimization, systems modeling, intelligent control, and production scheduling. The authors present the advanced features of EO in solving NP-hard problems through problem formulation, algorithms, and simulation studies on popular benchmarks and industrial applications. They also focus on the development of MEO and its applications. This book can be used as a reference for graduate students, research developers, and practical engineers who work on developing optimization solutions for those complex systems with hardness that cannot be solved with mathematical optimization or other computational intelligence, such as evolutionary computations.
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Övrig information

Professor Yong-Zai Lu (IEEE Fellow since 1998) earned his diploma degree from the Department of Chemical Engineering, Zhejiang University, China, in 1961, where he currently is an emeritus professor with the Institute of Cyber Systems and Control. He previously was a consulting professor at Shanghai Jiaotong University and a senior consultant at Supcon Co. China. During 1991 to 2003, he held senior consulting and technical positions at Bethlehem Steel Co., i2 Tech Inc. and Pavilion Tech Inc. in the US. He was a full professor and director of research at the Institute of Industrial Control, Zhejiang University, from 1984 to 1991. During 1980-1982, he was with Purdue University as a Visiting Scholar. He has supervised about 80 PhD and MS students. His research interests include system modeling, optimization, advanced control, intelligent control and computational intelligence, and their applications in production scale and real-world complex systems. He has authored and co-authored numerous SCI and EI papers and a number of books. He received National Science and Technology Progress Awards in China in 1989 and 1993, the ISA UOP Technology Award in 1989, and AISE Kelly Awards in the US in 1995 and 1996. He served as the President of IFAC from 1996 to 1999. Dr. Yu-Wang Chen is a lecturer in decision sciences at the University of Manchester, UK. Prior to his current appointment, he was a postdoctoral research associate at the Decision and Cognitive Sciences (DCS) research centre of Manchester Business School, the University of Manchester, and a postdoctoral research fellow at the Department of Computer Science, Hong Kong Baptist University. He earned his PhD degree from the Department of Automation, Shanghai Jiao Tong University in 2008. He has published over 30 journal and conference papers. His research interests include multiple criteria decision analysis under uncertainties, modeling and optimization of complex systems, and risk analysis in supply chains. Dr. Min-Rong Chen is an associate professor at the School of Computer, South China Normal University, China. She worked at the College of Information Engineering, Shenzhen University, China, from 2008 to 2015. She earned her PhD degree from the Department of Automation, Shanghai Jiao Tong University, China, in 2008. She has published over 20 journal and conference papers and has been PI for two Natural Science Foundation of China (NSFC) projects. Her research interests include evolutionary computation and information security. Dr. Peng Chen is a postdoctoral fellow at the Department of Control Science and Engineering, Zhejiang University, and research engineer at the Research Institute of Supcon Group. He earned his PhD degree from Shanghai Jiaotong University, China, in 2011. He has published over 10 journal and conference papers and been working on a number of production-scale research projects on industrial process modeling and control. His research interests include extremal dynamics and computaional intelligence, system modeling, and optimization control. Dr. Guo-Qiang Zeng is an associate professor at the Department of Electrical and Electronic Engineering, Wenzhou University, China. He earned his PhD degree in Control Science and Engineering from Zhejiang University, China, in 2011. He has published over 20 journal and conference papers. He received the Best Poster Paper Finalist and Best Student Paper Finalist from the 8th World Congress on Intelligent Control and Automation, 2010. He also received an NSFC funding on an extremal optimization oriented project. His research interests include computational intelligence, micro-grid, power electronics, complex networks, and discrete event systems.


FUNDAMENTALS, METHODOLOGY, AND ALGORITHMS General Introduction Introduction Understanding Optimization: From Practical Aspects Phase Transition and Computational Complexity CI-Inspired Optimization Highlights of EO Organization of the Book Introduction to Extremal Optimization Optimization with Extremal Dynamics Multidisciplinary Analysis of EO Experimental and Comparative Analysis on the Traveling Salesman Problems Summary Extremal Dynamics-Inspired Self-Organizing Optimization Introduction Analytic Characterization of COPs Self-Organized Optimization Summary MODIFIED EO AND INTEGRATION OF EO WITH OTHER SOLUTIONS TO COMPUTATIONAL INTELLIGENCE Modified Extremal Optimization Introduction Modified EO with Extended Evolutionary Probability Distribution Multistage EO Backbone-Guided EO Population-Based EO Summary Memetic Algorithms with Extremal Optimization Introduction to MAs Design Principle of MAs EO-LM Integration EO-SQP Integration EO-PSO Integration EO-ABC Integration EO-GA Integration Summary Multiobjective Optimization with Extremal Dynamics Introduction Problem Statement and Definition Solutions to Multiobjective Optimization EO for Numerical MOPs Multiobjective 0/1 Knapsack Problem with MOEO Mechanical Components Design with MOEO Portfolio Optimization with MOEO Summary APPLICATIONS EO for Systems Modeling and Control Problem Statement Endpoint Quality Prediction of Batch Production with MA-EO EO for Kernel Function and Parameter Optimization in Support Vector Regression Nonlinear Model Predictive Control with MA-EO Intelligent PID Control with Binary-Coded EO Summary EO for Production Planning and Scheduling Introduction Problem Formulation Hybrid Evolutionary Solutions with the Integration of GA and EO Summary References