ABSTRACT
摘要
CONTENTS
LIST OF TABLES
LIST OF FIGURES
INDEX OF ABBREVIATIONS
CHAPTER 1 Introduction
1．1 Background
1．2 Fundamental Concepts
1．3 Motivation
1．4 Contributions
1．5 Outline of the Thesis
CHAPTER 2 Investigating the Effect of Imbalance Between Convergence and Diversity in Evolutionary Multi-objective Algorithm
2．1 Overview
2．2 Introduction
2．3 Related Work
2．3．1 Convergence-first EMO Algorithms
2．3．2 MOEA／D-M2M
2．3．3 Related Theoretical Studies
2．4 Imbalanced Problems in Multi-objective Optimization
2．4．1 Definition of an Imbalanced Problem
2．4．2 Illustrative Problems
2．5 A Critical Review of Convergence-first EMO Methods for Imbalanced Problems
2．6 Numerical Computations on Imbalanced Problems
2．6．1 EMO Algorithms in the MOEA／D-M2M Framework
2．6．2 Proposed Imbalanced Multi-Objective Test Suite
2．7 Results of EMO and EMO-MOEA／D-M2M Methods
2．8 EMO-M2M for Balanced Problems
2．9 Conclusions
CHAPTER 3 Explicit Control of Implicit Parallelism in Decomposition Based Evolutionary Many-Objective Optimization Algorithms
3．1 Overview
3．2 Introduction
3．3 Preliminaries
3．3．2 NSGA-Ⅲ Framework
3．4 Explicit Control of Implicit Parallelism
3．5 Variants of M2M and Results
3．5．1 DTLZ and WFG Test Problems
3．5．2 Parameter Settings
3．5．3 Simulation Results on DTLZ Problems
3．5．4 Simulation Results on WFG Problems
3．6 Extended MOEA／D-M2M and MOEA／D Algorithms with Normaliza-tion
3．6．1 Simple Normalization Procedure
3．6．2 NSGA-Ⅲ Normalization Procedure on M2M and MOEA／D
3．7 NSGA-Ⅲ and MOEA／D Variants and Results
3．7．1 NSGA-Ⅲ Variants
3．7．2 MOEA／D Variants
3．8 Discussion on Explicit Control of Implicit Parallelism on EMO Algo-rithms
3．9 Conclusions
CHAPTER 4 Effect of Objective Normalization and Penalty Parameter on PBI Decomposition Based Evolutionary Many-objective Optimization Algorithms
4．1 Overview
4．2 Introduction
4．3 Decomposition-based EMO Algorithms
4．3．1 PBI Fitness
4．3．2 NSGA-Ⅲ’s Niching Fitness
4．4 Sensitivity of Fitness Assignment Due to Normalization Instability
4．4．1 Sensitivity Ratio
4．4．2 Validation
4．5 Experimental Studies
4．5．1 Test Problems
4．5．3 Experimental Studies on NSGA-Ⅲ
4．5．4 Experimental Studies on MOEA／D
4．5．5 Problems with a Convex Pareto-optimal Front
4．6 Conclusions
CHAPTER 5 Study on the Effect of Non-dominated Sorting in Decomposition Based Evolutionary Many-objective Optimization Algorithms
5．1 Overview
5．2 Introduction
5．3 Niching Mechanism in NSGA-Ⅲ
5．3．1 Theoretical Study
5．3．2 Experimental Validation
5．4 Why does non-dominated sorting matter?
5．4．1 Experimental Studies on Modified NSGA-Ⅲ
5．4．2 Mapping
5．4．3 Experimental studies
5．5 Conclusion
CHAPTER 6 Dynamic Search Resource Allocat ion for Many-objective Optimization
6．2 Introduction
6．3 Preliminaries
6．3．1 New Solution Generation
6．3．2 Update
6．4 Adaptive Subregion Division and Weight Vector Setting
6．4．2 Adaptive Weight Setting
6．4．3 Main Framework of MOEA／D-AM2M
6．5 Construction of Challenging MaOPs
6．5．1 Degenerated MaOPs with disconnected PFs
6．6 Experimental Study
6．6．1 EMO Algorithms in Comparison
6．6．2 Performance Metrics
6．6．3 Experimental Setting
6．6．4 Experimental Study on Degenerated MaOPs with Disconnected PFs
6．6．5 Further Performance Study of MOEA／D-AM2M on Degenerated MaOPs with Connected PFs
6．6．6 Experimental Study on Non-degenerated MaOPs
6．6．7 Experimental Study on Imbalanced MOPs
6．6．8 The Setting of Update Parameter f (G) in MOEA／D-AM2M
6．7 Conclusion
CHAPTER 7 Theoretical Studies on the Connection Among the Three Commonly Used Decomposition Methods
7．1 Overview
7．2 Introduction
7．3 Theoretical Study on Decomposition Methods
7．3．1 Decomposition Methods
7．3．2 Theoretical Study
7．4 Main Idea of Proposed Algorithm
7．4．1 Decomposition based Dominance Relationship
7．4．2 Properties Analysis
7．4．3 The Adaptive Setting of Parameter β
7．4．4 The novelty of D-dominance
7．4．5 Decomposition Based Crowding Measurement
7．4．6 Main Framework of Proposed Algorithm
7．5 Experimental Studies
7．5．1 EMO Algorithms in Comparison
7．5．2 Test Problems
7．5．4 Experimental Studies on WFG Test Problems
7．5．5 Experimental Studies on DTLZ Test Problems
7．6 Conclusion
CHAPTER 8 Modelling the Tracking Area Planning Problem Using an Evolutionary Multi-objective Algorithm
8．2 Introduction
8．3 Related Work
8．4 The TA Planning Problem
8．4．1 Problem Statement
8．4．2 Multi-objective TA Planning Model
8．5 An EMO Algorithm Based on the M2M Decomposition for the Multi-objective TA Planning Model
8．5．2 Decoding Method
8．5．3 Initialization Based on Fuzzy Clustering
8．5．4 Crossover and Mutation
8．5．6 MOEA／D and M2M Decomposition Strategy
8．5．7 Main Framework of the M2M-based EMO Algorithm for Multi-objective TA Planning
8．6 Computational Experiments and Analysis
8．6．1 The Parameters of the Networks
8．6．2 Experimental Results and Analysis
8．6．3 Computational Complexity
8．7 Conclusion
CHAPTER 9 Multi-objective Evolutionary Triclustering with Constraints of Time-series Gene Expression Data
9．2 Introduction
9．3 Preliminaries
9．3．2 Tridustering
9．4 Multi-objective constrained triclustering
9．5 Decomposition based evolutionary algorithm for multi-objective con-strained triclustering
9．5．1 Encoding and decoding
9．5．2 Recombination operators
9．5．3 Two-step local search
9．5．4 Multi-objective triclustering algorithm
9．6 Experimental studies
9．6．1 Performance metrics
9．6．2 Parameter setting
9．6．3 Experiments on artificial datasets
9．6．4 Experiments on real-life datasets
9．7 Engineering applications
9．7．1 Kev disease-related genes detection on HIV-1 progression data
9．7．2 Recommendation system for anonymous social network users
9．8 Conclusion and future work
Conclusions
References
List of Published／Submitted Papers
声明
ACKNOWLEDGEMENTS
