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Extremal Optimization: Fundamentals. Algorithms and Applications

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本书在总结编者近年来的原创性成果的基础上,综合了大量的EO相关文献,从原理、算法和应用等方面来介绍EO算法,内容横跨了多个学科,如运筹学、计算机软件、系统控制和制造工业等。本书的内容主要包括了以下四个方面。(1)本书重点探讨了具有较高计算复杂度的优化问题的解决方法,并对这些优化方法进行了归纳和总结。(2)针对一些标准测试问题,本书从原理、工作机理、算法和仿真实验等方面对EO算法内在的极值动力学机制及其应用进行了全面的介绍。另外,本书将EO算法与一些经典的启发式算法进行了仿真比较。(3)在总结编者近年来对EO的原创性研究成果的基础上,本书重点介绍了EO算法在自组织优化、进化概率分布和结构特征(例如骨架)等方面的工作机理。本书还介绍了各种改进的EO算法和基于EO的混合计算智能方法。(4)本书将EO算法和改进的EO算法应用于实际的工程领域,例如,多目标优化领域、生物信息学领域、系统建模和控制领域以及生产调度领域。本书对于从事自动控制优化工作的研究人员以及工程技术人员学习和掌握EO方法具有重要作用。<br/> <br/> <b>Editor's Recommendation</b> <p>本书的特色和价值:<br/> (1)尽管目前已有不少外文书籍介绍智能算法,但是这些书籍都没有介绍EO算法。这是一本从原理和实践两方面深入、系统地介绍EO算法的书籍;<br/> (2)本书将介绍许多实际的优化问题,并给出求解这些问题的EO算法伪代码和源代码,这将会有助于减少读者对实际问题的应用开发时间;<br/> (3)本书将EO算法的应用拓展到多个领域,例如生物信息学、交通拥塞控制、供应链管理、智能制造和网络优化等。</p> <br/> <br/> <br/> <br/> <b>About Author</b> <p>吕勇哉,浙江大学,教授,吕勇哉教授,美国电工电子工程师学会(IEEE) Fellow、国际自动控制联合会(IFAC)主席 (1996年~1999年)。成功地领导和研究开发了集成神经网络控制系统和遗传算法优化调度等系统。曾获美国仪器仪表学会UOP技术奖,于1995年和1996年连获美国钢铁学会Kelly奖,出版了《Industrial Intelligent Control: Fundamentals and Applications》等专著,并获得国家科学技术进步二等奖,全国科技图书一等奖,国家教委教学成果奖和多项部级奖。曾任中国自动化学会副理事长、国务院学位委员会和国家自然科学基金会自动化学科评审组成员和浙江大学学术委员会副主任等职。</p> <br/>  
Table of Contents
Preface
Acknowledgments
SECTION Ⅰ FUNDAMENTALS, METHODOLOGY, AND ALGORITHMS
1 General Introduction
1.1 Introduction
1.2 Understanding Optimization:From Practical Aspects
1.2.1 Mathematical Optimization
1.2.2 Optimization: From Practical Aspects
1.2.3 Example Applications of Optimization
1.2.4 Problem Solving for Optimization
1.3 Phase Transition and Computational Complexity
1.3.1 Computational Complexity in General
1.3.2 Phase Transitionin Computation
1.4 CI—Inspired Optimization
1.4.1 Evolutionary Computations
1.4.2 Swarm Intelligence
1.4.3 Data Mining and Machine Learning
1.4.4 Statistical Physics
1.5 Highlights of EO
1.5.1 Self—Organized Criticality and EO
1.5.2 Coevolution, Ecosystems, and Bak—Sneppen Model
1.5.3 Comparing EO with SA and GA
1.5.4 Challenging Open Problems
1.6 Organization of the Book
2 Introduction to Extremal Optimization
2.1 Optimization with Extremal Dynamics
2.2 Multidisciplinary Analysis of EO
2.3 Experimental and Comparative Analysis on the Traveling Salesman Problems
2.3.1 EO for the Symmetric TSP
2.3.1.1 Problem Formulation and Algorithm Design
2.3.2 SA versus Extremal Dynamics
2.3.3 Optimizing Near the Phase Transition
2.3.4 EO for the Asymmetric TSP
2.3.4.1 Cooperative Optimization
2.3.4.2 Parameter Analysis
2.4 Summary
3 Extremal Dynamics—Inspired Self—Organizing Optimization
3.1 Introduction
3.2 Analytic Characterization of COPs
3.2.1 Modeling COPs into Multientity Systems
3.2.2 Local Fitness Function
3.2.3 Microscopic Analysis of Optimal Solutions
3.2.4 Neighborhood and Fitness Network
3.2.5 Computational Complexity and Phase Transition
3.3 Self—Organized Optimization
3.3.1 Self—Organized Optimization Algorithm
3.3.2 Comparison with Related Methods
3.3.2.1 Simulated Annealing
3.3.2.2 Genetic Algorithm
3.3.2.3 Extremal Optimization
3.3.3 Experimental Validation
3.4 Summary
SECTION Ⅱ MODIFIED EO AND INTEGRATION OF EO WITH OTHER SOLUTIONS TO COMPUTATIONAL INTELLIGENCE
4 Modified Extremal Optimization
4.1 Introduction
4.2 Modified EO with Extended Evolurionary Probability Distribution
4.2.1 Evolutionary Probability Distribution
4.2.2 Modified EO Algorithm with Extended Evolutionary Probability Distribution
4.2.3 Experimental Results
4.3 Multistage EO
4.3.1 Motivations
4.3.2 MSEO Algorithm
4.3.3 Experimental Results
4.3.3.1 The Simplest Case: Two—Stage EO
4.3.3.2 Complex Case
4.3.4 Adjustable Parameters versus Performance
4.4 Backbone—Guided EO
4.4.1 Definitions of Fitness and Backbones
4.4.2 BGEO Algorithm
4.4.3 Experimental Results
4.5 Population—Based EO
4.5.1 Problem Formulation of Numerical Constrained Optimization Problems
4.5.2 PEO Algorithm
4.5.3 Mutation Operator
4.5.4 Experimental Results
4.5.5 Advantages of PEO
4.6 Summary
5 Memetic Algorithms with Extremal Optimization
5.1 Introduction to MAs
5.2 Design Principle of MAs
5.3 EO—LM Integration
5.3.1 Introduction
5.3.2 Problem Statement and Math Formulation
5.3.3 Introduction of LM GS
5.3.4 MA—Based Hybrid EO—LM Algorithm
5.3.5 Fitness Function
5.3.6 Experimental Tests on Benchmark Problems
5.3.6.1 A Multi—Input, Single—Output Static Nonlinear Function
5.3.6.2 Five—Dimensional Ackley Function Regression
5.3.6.3 Dynamic Modeling for Continuously Stirred Tank Reactor
5.4 EO—SQP Integration
5.4.1 Introduction
5.4.2 Problem Formularion
5.4,3 Introduction of SQP
5.4.4 MA—Based Hybrid EO—SQP Algorithm
5.4,5 Fitness Function Definition
5.4.6 Termination Criteria
5.4.7 Workflow and Algorithm
5.4.8 Experimental Tests on Benchmark Functions
5.4.8.1 Unconstrained Problems
5.4.8.2 Constrained Problems
5.4.9 Dynamics Analysis of the Hybrid EO—SQP
5.5 EO—PSO Integration
5.5.1 Introduction
5.5.2 Particle Swarm Optimization
5.5.3 PSO—EO Algorithm
5.5.4 Mutation Operator
5.5.5 Computational Complexity
5.5.6 Experimental Results
5.6 EO—ABC Integration
5.6.1 Artificial Bee Colony
5.6.2 ABC—EO Algorithm
5.6.3 Mutation Operator
5.6.4 DifFerences between ABC—EO and Other Hybrid Algorithms
5.6.5 Experimental Results
5.7 EO—GA Integration
5.8 Summary
6 Multiobjective Optimization with Extremal Dynamics
6.1 Introduction
6.2 Problem Statement and Definition
6.3 Solutions to Multiobjective Optimization
6.3.1 Aggregating Functions
6.3.2 Population—Based Non—Pareto Approaches
6.3.3 Pareto—Based Approaches
6.4 EO for Numerical MOPs
6.4.1 MOEO Algorithm
6.4.1.1 Fitness Assignment
6.4.1.2 Diversity Preservation
6.4.1.3 External Archive
6.4.1.4 Mutation Operation
6.4.2 Unconstrained Numerical MOPs with MOEO
6.4.2.1 Performance Metrics
6.4.2.2 Experimental Settings
6.4.2.3 Experimental Results and Discussion
6,4.2.4 Conclusions
6.4.3 Constrained Numerical MOPs with MOEO
6.4.3.1 Performance Metrics
6.4.3.2 Experimental Settings
6.4.3.3 Experimental Results and Discussion
6.4.3.4 Conclusions
6.5 Multiobjective 0/1 Knapsack Problem with MOEO
6.5.1 Extended MOEO for MOKP
6.5.1.1 Mutation Operation
6.5.1.2 Repair Strategy
6.5.2 Experimental Settings
6.5.3 Experimental Results and Discussion
6.5.4 Conclusions
6.6 Mechanical Components Design with MOEO
6.6.1 Introduction
6.6.2 Experimental Settings
6.6.2.1 Two—Bar Truss Design (Two Bar for Short)
6.6.2.2 Welded Beam Design (Welded Beam for Short)
6.6.2.3 Machine Tool Spindle Design (Spindle for Short)
6.6.3 Experimental Results and Discussion
6.6.4 Conclusions
6.7 Portfolio Optimization with MOEO
6.7.1 Portfolio Optimization Model
6.7.2 MOEO for Portfolio Optimization Problems
6.7.2.1 Mutation Operation
6.7.2.2 Repair Strategy
6.7.3 Experimental Settings
6.7.4 Experimental Results and Discussion
6.7.5 Conclusions
6.8 Summary
……
SECTION Ⅲ APPLICATIONS
References
Author Index
Subject Index
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Sample pages of Extremal Optimization: Fundamentals. Algorithms and Applications (ISBN:9787122270016)

Sample pages of Extremal Optimization: Fundamentals. Algorithms and Applications (ISBN:9787122270016)

Sample pages of Extremal Optimization: Fundamentals. Algorithms and Applications (ISBN:9787122270016)

Sample pages of Extremal Optimization: Fundamentals. Algorithms and Applications (ISBN:9787122270016)

Sample pages of Extremal Optimization: Fundamentals. Algorithms and Applications (ISBN:9787122270016)



Preface

With the high demand and the critical situation of solving hard optimization problems we are facing in social, environment, bioinformatics, traffic, and indus-trial systems, the development of more efficient novel optimization solutions has been a serious challenge to academic and practical societies in an information-rich era. In addition to the traditional math-programming-inspired optimiza-tion solutions, computational intelligence has been playing an important role in developing novel optimization solutions for practical applications. On the basis of the features of system complexity, a new general-purpose heuristic for finding high-quality solutions to NP-hard (nondeterministic polynomial-time) optimi-zation problems, the so-called “extremal optimization (EO),” was proposed by Boettcher and Percus. In principle, this method is inspired by the Bak–Sneppen model of self-organized criticality describing “far-from-equilibrium phenomena,” from statistical physics, a key concept describing the complexity in physical sys-tems. In comparison with other modern heuristics, such as simulated anneal-ing, genetic algorithm (GA), through testing on some popular benchmarks (TSP [traveling salesman problem], coloring, K-SAT, spin glass, etc.) of large-scale combinatory-constrained optimization problems, EO shows superior performance in the convergence and capability of dealing with computational complexity, for example, the phase transition in search dynamics and having much fewer tuning parameters.
The aim of this book is to introduce the state-of-the-art EO solutions from fun-damentals, methodologies, and algorithms to applications based on numerous clas-sic publications and the authors’ recent original research results, and to make EO more popular with multidisciplinary aspects, such as operations research, software, systems control, and manufacturing. Hopefully, this book will promote the move-ment of EO from academic study to practical applications. It should be noted that EO has a strong basic science foundation in statistical physics and bioevolution, but from the application point of view, compared with many other metaheuristics, the application of EO is much simpler, easier, and straightforward. With more studies in EO search dynamics, the hybrid solutions with the marriage of EO and other metaheuristics, and the real-world application, EO will be an additional weapon to deal with hard optimization problems. The contents of this book cover the follow-ing four aspects:
1. General review for 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.
2. General introduction to computational extremal dynamics and its applica-tions 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. In addition, the comparisons of EO with some popular heuristics, for example, simulated annealing and GA, are given through analytical and simulation studies.
3. The 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) are based on the authors’ recent research results. Moreover, modified extremal optimization (MEO) solutions and memetic algorithms are presented.
4. On the basis of the authors’ research results, the applications of EO and MEO in multiobjective optimization, systems modeling, intelligent control, and production scheduling are presented.

The authors have made great efforts to focus on the development of MEO and its applications, and also present the advanced features of EO in solving NP-hard problems through problem formulation, algorithms, and simulation studies on popular benchmarks and industrial applications. This book can be used as a refer-ence for graduate students, research developers, and practical engineers when they work on developing optimization solutions for those complex systems with hard-ness that cannot be solved with mathematical optimization or other computational intelligence, such as evolutionary computations. This book is divided into the fol-lowing three parts.
Section I: Chapter 1 provides the general introduction to optimization with a focus on computational complexity, computational intelligence, the highlights of EO, and the organization of the book; Chapter 2 introduces the fundamental and numerical examples of extremal dynamics-inspired EO; and Chapter 3 presents the extremal dynamics–inspired self-organizing optimization.
Section II: Chapter 4 covers the development of modified EO, such as pop-ulation-based EO, multistage EO, and modified EO with an extended evolu-tionary probability distribution. Chapter 5 presents the development of memetic algorithms, the integration of EO with other computational intelligence, such as GA, particle swarm optimization (PSO), and artificial bee colony (ABC). Chapter 6 presents the development of multiobjective optimization with extremal dynamics.
Section III includes the applications of EO in nonlinear modeling and predic-tive control, and production planning and scheduling, covered in Chapters 7 and 8, respectively.

Extremal Optimization: Fundamentals. Algorithms and Applications
$25.80