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E-raamat: Extremal Optimization: Fundamentals, Algorithms, and Applications

(University of Manchester, UK), (Wenzhou Univer), (Shenzhen University, PR of China), (Zhejiang University, Hangzhou, PR of China), (Research Institute of Supcon Group, Binjiang District, Hangzhou, Zhejiang, People's Republic of China)
  • Formaat: 334 pages
  • Ilmumisaeg: 21-Apr-2016
  • Kirjastus: Auerbach Publishers Inc.
  • Keel: eng
  • ISBN-13: 9781498705660
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Extremal Optimization: Fundamentals, Algorithms, and ApplicationsSuurem pilt
  • Formaat: 334 pages
  • Ilmumisaeg: 21-Apr-2016
  • Kirjastus: Auerbach Publishers Inc.
  • Keel: eng
  • ISBN-13: 9781498705660
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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.
Preface xi
Acknowledgments xv
SECTION I FUNDAMENTALS, METHODOLOGY, AND ALGORITHMS
1 General Introduction
3(18)
1.1 Introduction
3(1)
1.2 Understanding Optimization: From Practical Aspects
4(5)
1.2.1 Mathematical Optimization
4(1)
1.2.2 Optimization: From Practical Aspects
5(1)
1.2.3 Example Applications of Optimization
6(2)
1.2.4 Problem Solving for Optimization
8(1)
1.3 Phase Transition and Computational Complexity
9(2)
1.3.1 Computational Complexity in General
9(1)
1.3.2 Phase Transition in Computation
10(1)
1.4 CI-Inspired Optimization
11(3)
1.4.1 Evolutionary Computations
11(1)
1.4.2 Swarm Intelligence
12(1)
1.4.3 Data Mining and Machine Learning
13(1)
1.4.4 Statistical Physics
13(1)
1.5 Highlights of EO
14(4)
1.5.1 Self-Organized Criticality and EO
14(2)
1.5.2 Coevolution, Ecosystems, and Bak--Sneppen Model
16(1)
1.5.3 Comparing EO with SA and GA
17(1)
1.5.4 Challenging Open Problems
17(1)
1.6 Organization of the Book
18(3)
2 Introduction to Extremal Optimization
21(16)
2.1 Optimization with Extremal Dynamics
21(2)
2.2 Multidisciplinary Analysis of EO
23(1)
2.3 Experimental and Comparative Analysis on the Traveling Salesman Problems
24(1)
23.1 EO for the Symmetric TSP
25(10)
2.3.1.1 Problem Formulation and Algorithm Design
25(2)
2.3.2 SA versus Extremal Dynamics
27(3)
2.3.3 Optimizing Near the Phase Transition
30(1)
2.3.4 EO for the Asymmetric TSP
31(1)
2.3.4.1 Cooperative Optimization
32(1)
2.3.4.2 Parameter Analysis
33(2)
2.4 Summary
35(2)
3 Extremal Dynamics--Inspired Self-Organizing Optimization
37(24)
3.1 Introduction
37(2)
3.2 Analytic Characterization of COPs
39(12)
3.2.1 Modeling COPs into Multientity Systems
39(1)
3.2.2 Local Fitness Function
40(3)
3.2.3 Microscopic Analysis of Optimal Solutions
43(3)
3.2.4 Neighborhood and Fitness Network
46(3)
3.2.5 Computational Complexity and Phase Transition
49(2)
3.3 Self-Organized Optimization
51(6)
3.3.1 Self-Organized Optimization Algorithm
51(2)
3.3.2 Comparison with Related Methods
53(1)
3.3.2.1 Simulated Annealing
54(1)
3.3.2.2 Genetic Algorithm
54(1)
3.3.2.3 Extremal Optimization
55(1)
3.3.3 Experimental Validation
55(2)
3.4 Summary
57(4)
SECTION II MODIFIED EO AND INTEGRATION OF EO WITH OTHER SOLUTIONS TO COMPUTATIONAL INTELLIGENCE
4 Modified Extremal Optimization
61(40)
4.1 Introduction
61(1)
4.2 Modified EO with Extended Evolutionary Probability Distribution
61(9)
4.2.1 Evolutionary Probability Distribution
62(2)
4.2.2 Modified EO Algorithm with Extended Evolutionary Probability Distribution
64(3)
4.2.3 Experimental Results
67(3)
4.3 Multistage EO
70(8)
4.3.1 Motivations
70(2)
4.3.2 MSEO Algorithm
72(1)
4.3.3 Experimental Results
73(1)
4.3.3.1 The Simplest Case: Two-Stage EO
73(1)
4.3.3.2 Complex Case
74(3)
4.3.4 Adjustable Parameters versus Performance
77(1)
4.4 Backbone-Guided EO
78(10)
4.4.1 Definitions of Fitness and Backbones
80(1)
4.4.2 BGEO Algorithm
81(3)
4.4.3 Experimental Results
84(4)
4.5 Population-Based EO
88(10)
4.5.1 Problem Formulation of Numerical Constrained Optimization Problems
90(1)
4.5.2 PEO Algorithm
91(1)
4.5.3 Mutation Operator
92(2)
4.5.4 Experimental Results
94(3)
4.5.5 Advantages of PEO
97(1)
4.6 Summary
98(3)
5 Memetic Algorithms with Extremal Optimization
101(64)
5.1 Introduction to MAs
101(1)
5.2 Design Principle of MAs
102(3)
5.3 EO--LM Integration
105(14)
5.3.1 Introduction
105(2)
5.3.2 Problem Statement and Math Formulation
107(1)
5.3.3 Introduction of LM GS
108(1)
5.3.4 MA-Based Hybrid EO--LM Algorithm
109(4)
5.3.5 Fitness Function
113(1)
5.3.6 Experimental Tests on Benchmark Problems
114(1)
5.3.6.1 A Multi-Input, Single-Output Static Nonlinear Function
114(2)
5.3.6.2 Five-Dimensional Ackley Function Regression
116(1)
5.3.6.3 Dynamic Modeling for Continuously Stirred Tank Reactor
116(3)
5.4 EO--SQP Integration
119(19)
5.4.1 Introduction
119(2)
5.4.2 Problem Formulation
121(1)
5.4.3 Introduction of SQP
122(1)
5.4.4 MA-Based Hybrid EO--SQP Algorithm
123(2)
5.4.5 Fitness Function Definition
125(1)
5.4.6 Termination Criteria
125(1)
5.4.7 Workflow and Algorithm
126(1)
5.4.8 Experimental Tests on Benchmark Functions
127(1)
5.4.8.1 Unconstrained Problems
128(4)
5.4.8.2 Constrained Problems
132(4)
5.4.9 Dynamics Analysis of the Hybrid EO--SQP
136(2)
5.5 EO--PSO Integration
138(11)
5.5.1 Introduction
138(1)
5.5.2 Particle Swarm Optimization
139(1)
5.5.3 PSO--EO Algorithm
140(1)
5.5.4 Mutation Operator
140(3)
5.5.5 Computational Complexity
143(1)
5.5.6 Experimental Results
143(6)
5.6 EO--ABC Integration
149(11)
5.6.1 Artificial Bee Colony
150(3)
5.6.2 ABC--EO Algorithm
153(1)
5.6.3 Mutation Operator
154(1)
5.6.4 Differences between ABC--EO and Other Hybrid Algorithms
155(1)
5.6.5 Experimental Results
155(5)
5.7 EO--GA Integration
160(3)
5.8 Summary
163(2)
6 Multiobjective Optimization with Extremal Dynamics
165(50)
6.1 Introduction
165(2)
6.2 Problem Statement and Definition
167(1)
6.3 Solutions to Multiobjective Optimization
168(2)
6.3.1 Aggregating Functions
168(1)
6.3.2 Population-Based Non-Pareto Approaches
169(1)
6.3.3 Pareto-Based Approaches
169(1)
6.4 EO for Numerical MOPs
170(21)
6.4.1 MOEO Algorithm
171(1)
6.4.1.1 Fitness Assignment
171(2)
6.4.1.2 Diversity Preservation
173(1)
6.4.1.3 External Archive
174(1)
6.4.1.4 Mutation Operation
175(1)
6.4.2 Unconstrained Numerical MOPs with MOEO
176(1)
6.4.2.1 Performance Metrics
176(3)
6.4.2.2 Experimental Settings
179(1)
6.4.2.3 Experimental Results and Discussion
179(6)
6.4.2.4 Conclusions
185(1)
6.4.3 Constrained Numerical MOPs with MOEO
185(1)
6.4.3.1 Performance Metrics
186(1)
6.4.3.2 Experimental Settings
187(1)
6.4.3.3 Experimental Results and Discussion
188(3)
6.4.3.4 Conclusions
191(1)
6.5 Multiobjective 0/1 Knapsack Problem with MOEO
191(6)
6.5.1 Extended MOEO for MOKP
191(1)
6.5.1.1 Mutation Operation
191(1)
6.5.1.2 Repair Strategy
192(1)
6.5.2 Experimental Settings
193(1)
6.5.3 Experimental Results and Discussion
194(1)
6.5.4 Conclusions
195(2)
6.6 Mechanical Components Design with MOEO
197(6)
6.6.1 Introduction
197(1)
6.6.2 Experimental Settings
198(1)
6.6.2.1 Two-Bar Truss Design (Two Bar for Short)
198(1)
6.6.2.2 Welded Beam Design (Welded Beam for Short)
198(1)
6.6.2.3 Machine Tool Spindle Design (Spindle for Short)
199(2)
6.6.3 Experimental Results and Discussion
201(1)
6.6.4 Conclusions
202(1)
6.7 Portfolio Optimization with MOEO
203(9)
6.7.1 Portfolio Optimization Model
203(2)
6.7.2 MOEO for Portfolio Optimization Problems
205(1)
6.7.2.1 Mutation Operation
206(1)
6.7.2.2 Repair Strategy
207(1)
6.7.3 Experimental Settings
207(1)
6.7.4 Experimental Results and Discussion
208(4)
6.7.5 Conclusions
212(1)
6.8 Summary
212(3)
SECTION III APPLICATIONS
7 EO for Systems Modeling and Control
215(56)
7.1 Problem Statement
215(1)
7.2 Endpoint Quality Prediction of Batch Production with MA-EO
216(3)
7.3 EO for Kernel Function and Parameter Optimization in Support Vector Regression
219(19)
7.3.1 Introduction
221(1)
7.3.2 Problem Formulation
221(1)
7.3.2.1 Support Vector Regression
222(1)
7.3.2.2 Optimization of SVR Kernel Function and Parameters
223(1)
7.3.3 Hybrid EO-Based Optimization for SVR Kernel Function and Parameters
224(1)
7.3.3.1 Chromosome Structure
224(1)
7.3.3.2 Fitness Function
225(1)
7.3.3.3 EO-SVR Workflow
226(2)
7.3.4 Experimental Results
228(1)
7.3.4.1 Approximation of Single-Variable Function
228(5)
7.3.4.2 Approximation of Multivariable Function
233(5)
7.4 Nonlinear Model Predictive Control with MA-EO
238(14)
7.4.1 Problem Formulation for NMPC Based on SVM Model
239(3)
7.4.2 Real-Time NMPC with SVM and EO-SQP
242(1)
7.4.2.1 Workflow of Proposed NMPC
242(2)
7.4.2.2 Encoding Strategy
244(2)
7.4.2.3 Selection of the Initial Population
246(1)
7.4.2.4 Termination Criteria of the NLP Solver
246(1)
7.4.2.5 Horizon-Based EO Mutation for NMPC Online Optimization
246(1)
7.4.3 Simulation Studies
247(5)
7.5 Intelligent PID Control with Binary-Coded EO
252(15)
7.5.1 PID Controllers and Performance Indices
252(3)
7.5.2 BCEO Algorithm
255(3)
7.5.3 Experimental Results
258(1)
7.5.3.1 Single-Variable Controlled Plant
258(3)
7.5.3.2 Multivariable Controlled Plant
261(6)
7.5.3.3 Parameters and Control Performances
267(1)
7.6 Summary
267(4)
8 EO for Production Planning and Scheduling
271(26)
8.1 Introduction
271(5)
8.1.1 An Overview of HSM Scheduling
272(1)
8.1.2 Production Process
273(1)
8.1.3 Scheduling Objectives and Constraints
274(2)
8.2 Problem Formulation
276(4)
8.3 Hybrid Evolutionary Solutions with the Integration of GA and EO
280(16)
8.3.1 Global Search Algorithm: Modified GA for Order Selection and Sequencing
280(1)
8.3.1.1 Representation of Solutions
280(1)
8.3.1.2 Population Initialization
281(1)
8.3.1.3 Fitness Function
282(1)
8.3.1.4 Genetic Operators
282(3)
8.3.2 Local Improving Algorithm: τ-EO
285(1)
8.3.2.1 Introduction to EO
286(1)
8.3.2.2 EO for Improving the Body Section
286(2)
8.3.2.3 Hybrid Evolutionary Algorithms
288(3)
8.3.3 Design of a HSM-Scheduling System
291(2)
8.3.4 Computational Results
293(3)
8.4 Summary
296(1)
References 297(18)
Author Index 315(8)
Subject Index 323
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.
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