簡體書 » 極值優化：原理、算法和應用(英文版)

極值優化：原理、算法和應用(英文版)

定價：~~768~~ 元

NT $ 576

作者：呂勇哉
出版社：化學工業出版社
出版日期：2016-08-01
語言：簡體中文
ISBN10：7122270017
ISBN13：9787122270016
裝訂：334頁 / 普通級 / 1-1

內容簡介

本書在總結編者近年來的原創性成果的基礎上，綜合了大量的EO相關文獻，從原理、算法和應用等方面來介紹EO算法，內容橫跨了多個學科，如運籌學、計算機軟件、系統控制和制造工業等。

本書主要包括了以下四個方面。
（1）本書重點探討了具有較高計算復雜度的優化問題的解決方法，並對這些優化方法進行了歸納和總結。

（2）針對一些標准測試問題，本書從原理、工作機理、算法和仿真實驗等方面對EO算法內在的極值動力學機制及其應用進行了全面的介紹。另外，本書將EO算法與一些經典的啟發式算法進行了仿真比較。

（3）在總結編者近年來對EO的原創性研究成果的基礎上，本書重點介紹了EO算法在自組織優化、進化概率分布和結構特征（例如骨架）等方面的工作機理。本書還介紹了各種改進的EO算法和基於EO的混合計算智能方法。

（4）本書將EO算法和改進的EO算法應用於實際的工程領域，例如，多目標優化領域、生物信息學領域、系統建模和控制領域以及生產調度領域。

本書對於從事自動控制優化工作的研究人員以及工程技術人員學習和掌握EO方法具有重要作用。

呂勇哉，浙江大學，教授，呂勇哉教授，美國電工電子工程師學會(IEEE) Fellow、國際自動控制聯合會(IFAC)主席 (1996年~1999年)。成功地領導和研究開發了集成神經網絡控制系統和遺傳算法優化調度等系統。曾獲美國儀器儀表學會UOP技術獎，於1995年和 1996年連獲美國鋼鐵學會Kelly獎，出版了《Industrial Intelligent Control: Fundamentals and Applications》等專着，並獲得國家科學技術進步二等獎，全國科技圖書一等獎，國家教委教學成果獎和多項部級獎。曾任中國自動化學會副理事長、國務院學位委員會和國家自然科學基金會自動化學科評審組成員和浙江大學學術委員會副主任等職。

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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