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E-raamat: Multiobjective Optimization Methodology: A Jumping Gene Approach

(City University of Hong Kong, China), (City University of Hong Kong, China), (City University of Hong Kong, China), (City University of Hong Kong, China)
  • Formaat: 279 pages
  • Sari: Industrial Electronics
  • Ilmumisaeg: 03-Sep-2018
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351832526
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Multiobjective Optimization Methodology: A Jumping Gene ApproachSuurem pilt
  • Formaat: 279 pages
  • Sari: Industrial Electronics
  • Ilmumisaeg: 03-Sep-2018
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351832526
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The first book to focus on jumping genes outside bioscience and medicine, Multiobjective Optimization Methodology: A Jumping Gene Approach introduces jumping gene algorithms designed to supply adequate, viable solutions to multiobjective problems quickly and with low computational cost.

Better Convergence and a Wider Spread of Nondominated Solutions

The book begins with a thorough review of state-of-the-art multiobjective optimization techniques. For readers who may not be familiar with the bioscience behind the jumping gene, it then outlines the basic biological gene transposition process and explains the translation of the copy-and-paste and cut-and-paste operations into a computable language.

To justify the scientific standing of the jumping genes algorithms, the book provides rigorous mathematical derivations of the jumping genes operations based on schema theory. It also discusses a number of convergence and diversity performance metrics for measuring the usefulness of the algorithms.

Practical Applications of Jumping Gene Algorithms

Three practical engineering applications showcase the effectiveness of the jumping gene algorithms in terms of the crucial trade-off between convergence and diversity. The examples deal with the placement of radio-to-fiber repeaters in wireless local-loop systems, the management of resources in WCDMA systems, and the placement of base stations in wireless local-area networks.

Offering insight into multiobjective optimization, the authors show how jumping gene algorithms are a useful addition to existing evolutionary algorithms, particularly to obtain quick convergence solutions and solutions to outliers.

Arvustused

"This is an interesting and practical book. It is easy to read [ and] provides good background information ... [ and] cutting-edge technologies to solve the challenging multi-objective optimization problems." Mo-Yuen Chow, North Carolina State University, Raleigh, USA

"The authors describe the jumping gene approach to solve multiobjective optimization problems. It is quite [ a] new approach and complements standard operations used in genetic algorithms." Marcin Anholcer (Poznan), Zentralblatt MATH 1273

Preface ix
About the Authors xi
1 Introduction
1(8)
1.1 Background on Genetic Algorithms
1(3)
1.2 Organization of
Chapters
4(5)
References
5(4)
2 Overview of Multiobjective Optimization
9(24)
2.1 Classification of Optimization Methods
9(2)
2.1.1 Enumerative Methods
9(1)
2.1.2 Deterministic Methods
9(1)
2.1.3 Stochastic Methods
10(1)
2.2 Multiobjective Algorithms
11(22)
2.2.1 Multiobjective Genetic Algorithm
11(2)
2.2.1.1 Modified Fitness Assignment
13(1)
2.2.1.2 Fitness Sharing
13(1)
2.2.2 Niched Pareto Genetic Algorithm 2
14(1)
2.2.3 Nondominated Sorting Genetic Algorithm 2
15(1)
2.2.3.1 Fast Nondominated Sorting Approach
15(2)
2.2.3.2 Crowded-Comparison Approach
17(2)
2.2.3.2 Elitism Strategy
19(1)
2.2.4 Strength Pareto Evolutionary Algorithm 2
19(1)
2.2.4.1 Strength Value and Raw Fitness
20(1)
2.2.4.2 Density Estimation
20(2)
2.2.4.3 Archive Truncation Method
22(1)
2.2.5 Pareto Archived Evolution Strategy
22(1)
2.2.6 Microgenetic Algorithm
23(1)
2.2.6.1 Population Memory
24(1)
2.2.6.2 Adaptive Grid Algorithm
24(1)
2.2.6.3 Three Types of Elitism
25(1)
2.2.7 Ant Colony Optimization
25(2)
2.2.8 Particle Swarm Optimization
27(1)
2.2.9 Tabu Search
28(1)
References
29(4)
3 Jumping Gene Computational Approach
33(20)
3.1 Biological Background
33(3)
3.1.1 Biological Jumping Gene Transposition
33(2)
3.1.2 Advantageous Effects of JG on Host Evolution
35(1)
3.2 Overview of Computational Gene Transposition
36(5)
3.2.1 Sexual or Asexual Transposition
36(2)
3.2.2 Bacterial Operations
38(1)
3.2.2.1 Transduction
38(1)
3.2.2.2 Conjugation
39(1)
3.2.2.3 Transformation
40(1)
3.2.3 Other Operations
41(1)
3.3 Jumping Gene Genetic Algorithms
41(4)
3.3.1 Transposons in Chromosomes
42(1)
3.3.2 Cut-and-Paste and Copy-and-Paste Operations
42(1)
3.3.3 Jumping Gene Transposition
43(1)
3.3.4 Some Remarks
44(1)
3.4 Real-Coding Jumping Operations
45(8)
References
49(4)
4 Theoretical Analysis of Jumping Gene Operations
53(36)
4.1 Overview of Schema Models
53(4)
4.1.1 Schema
53(1)
4.1.2 Holland's Model
53(2)
4.1.3 Stephens and Waelbroeck's Model
55(2)
4.2 Exact Schema Theorem for Jumping Gene Transposition
57(12)
4.2.1 Notations and Functional Definitions
57(1)
4.2.1.1 Notations
57(1)
4.2.1.2 Functional Definitions
57(2)
4.2.2 Exact Schema Evolution Equation for Copy-and-Paste
59(5)
4.2.3 Exact Schema Evolution Equation for Cut-and-Paste
64(5)
4.3 Theorems of Equilibrium and Dynamical Analysis
69(10)
4.3.1 Distribution Matrix for Copy-and-Paste
69(3)
4.3.2 Distribution Matrix for Cut-and-Paste
72(1)
4.3.3 Lemmas
72(3)
4.3.4 Proof of Theorem 4.1
75(3)
4.3.5 Proof of Theorem 4.2
78(1)
4.4 Simulation Results and Analysis
79(1)
4.4.1 Simulation 4.1: Existence of Equilibrium
79(1)
4.4.2 Simulation 4.2: Primary Schemata Competition Sets with Different Orders
80(1)
4.5 Discussion
80(9)
4.5.1 Assumptions
80(1)
4.5.2 Implications
80(2)
4.5.3 Destruction and Construction
82(1)
4.5.4 Finite Population Effect
83(1)
4.5.5 The Effect of the JG in a GA
84(3)
References
87(2)
5 Performance Measures on Jumping Gene
89(40)
5.1 Convergence Metric: Generational Distance
89(1)
5.2 Convergence Metric: Deb and Jain Convergence Metric
90(1)
5.3 Diversity Metric: Spread
91(1)
5.4 Diversity Metric: Extreme Nondominated Solution Generation
92(2)
5.5 Binary ε-Indicator
94(1)
5.6 Statistical Test Using Performance Metrics
95(1)
5.7 Jumping Gene Verification and Results
96(33)
5.7.1 JG Parameter Study
96(2)
5.7.2 Comparisons with Other MOEAs
98(1)
5.7.2.1 Mean and Standard Deviation of Generational Distance for Evaluating Convergence
99(1)
5.7.2.2 Mean and Standard Deviation of Spread for Evaluating Diversity
100(8)
5.7.2.3 Diversity Evaluation Using Extreme Nondominated Solution Generation
108(1)
5.7.2.4 Statistical Test Using Binary ε-Indicator
108(3)
5.7.3 An Experimental Test of Theorems of Equilibrium
111(9)
5.7.3.1 Optimization of Controller Design
120(1)
5.7.3.2 Results and Comparisons
121(5)
References
126(3)
6 Radio-to-Fiber Repeater Placement in Wireless Local-Loop Systems
129(20)
6.1 Introduction
129(3)
6.2 Path Loss Model
132(1)
6.3 Mathematical Formulation
133(2)
6.4 Chromosome Representation
135(1)
6.5 Jumping Gene Transposition
136(1)
6.6 Chromosome Repairing
136(1)
6.7 Results and Discussion
137(12)
6.7.1 Mean and Standard Deviation of Deb and Jain Convergence Metric for Evaluating Convergence
139(1)
6.7.2 Mean and Standard Deviation of Spread for Evaluating Diversity
139(1)
6.7.3 Diversity Evaluation Using Extreme Nondominated Solution Generation
139(1)
6.7.4 Statistical Test Using Binary e-Indicator
139(8)
References
147(2)
7 Resource Management in WCDMA
149(30)
7.1 Introduction
149(2)
7.2 Mathematical Formulation
151(2)
7.3 Chromosome Representation
153(1)
7.4 Initial Population
154(1)
7.4.1 Power Generation
154(1)
7.4.2 Rate Generation
154(1)
7.5 Jumping Gene Transposition
154(1)
7.6 Mutation
155(2)
7.7 Ranking Rule
157(1)
7.8 Results and Discussion
157(12)
7.8.1 Mean and Standard Deviation of Deb and Jain Convergence Metric for Evaluating Convergence
161(1)
7.8.2 Mean and Standard Deviation of Spread for Evaluating Diversity
162(1)
7.8.3 Diversity Evaluation Using Extreme Nondominated Solution Generation
163(1)
7.8.4 Statistical Test Using Binary ε-Indicator
164(5)
7.9 Discussion of Real-Time Implementation
169(10)
References
177(2)
8 Base Station Placement in WLANs
179(22)
8.1 Introduction
179(1)
8.2 Path Loss Model
180(1)
8.3 Mathematical Formulation
181(2)
8.4 Chromosome Representation
183(1)
8.5 Jumping Gene Transposition
184(1)
8.6 Chromosome Repairing
184(1)
8.7 Results and Discussion
185(16)
8.7.1 Mean and Standard Deviation of Deb and Jain Convergence Metric for Evaluating Convergence
186(1)
8.7.2 Mean and Standard Deviation of Spread for Evaluating Diversity
186(1)
8.7.3 Diversity Evaluation Using Extreme Nondominated Solution Generation
187(2)
8.7.4 Statistical Test Using the Binary ε-Indicator
189(10)
References
199(2)
9 Conclusions
201(2)
References
202(1)
Appendix A Proofs of Lemmas in
Chapter 4		 203(18)
Appendix B Benchmark Test Functions 221(8)
Appendix C Chromosome Representation 229(2)
Appendix D Design of the Fuzzy PID Controller 231(6)
Index 237
Kit Sang Tang received his BSc from the University of Hong Kong in 1988 and his MSc and PhD from City University of Hong Kong in 1992 and 1996, respectively. He is currently an associate professor in the Department of Electronic Engineering at City University of Hong Kong. He has published over 90 journal papers and five book chapters, and coauthored two books, focusing on genetic algorithms and chaotic theory.

Tak Ming Chan received his BSc in applied physics from Hong Kong Baptist University in 1999 and his MPhil and PhD in electronic engineering from City University of Hong Kong in 2001 and 2006 respectively. He was a research associate in the Department of Industrial and Systems Engineering at the Hong Kong Polytechnic University from 2006 to 2007 and a postdoctoral fellow in the Department of Production and Systems Engineering, University of Minho, Portugal from 2007 to 2009.

Richard Jacob Yin obtained his BEng in Information Technology in 2004 and his PhD in Electronic Engineering in 2010 from the City University of Hong Kong. He is now an Electronic Engineer at ASM Assembly Automation Hong Kong Limited.

Kim Fung Man is a Chair Professor and head of the electronic engineering department at City University of Hong Kong. He received his PhD from Cranfield Institute of Technology, UK. He is currently the co-editor-in-chief of IEEE Transactions of Industrial Electronics. He has co-authored three books and published extensively in the area.
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