多目標(biāo)進(jìn)化算法理論、算法設(shè)計(jì)與應(yīng)用研究
發(fā)布時(shí)間：2021-08-04 18:07
多目標(biāo)優(yōu)化問題普遍存在于社會生活、生產(chǎn)的各個(gè)領(lǐng)域,但是用傳統(tǒng)的數(shù)學(xué)方法來求解多目標(biāo)優(yōu)化問題并不能取得很好的效果。近年來,隨著計(jì)算機(jī)技術(shù)的發(fā)展和人工智能的興起,基于計(jì)算機(jī)技術(shù)的計(jì)算智能得到了快速的發(fā)展。作為計(jì)算智能的一個(gè)重要分支,進(jìn)化計(jì)算求解多目標(biāo)優(yōu)化問題已經(jīng)成為計(jì)算智能領(lǐng)域的一個(gè)研究熱點(diǎn)。進(jìn)化計(jì)算最初是指受生物進(jìn)化啟發(fā)而設(shè)計(jì)的基于種群的優(yōu)化算法,現(xiàn)在己經(jīng)發(fā)展成為各種受自然啟發(fā)的算法和技術(shù)的統(tǒng)稱。如今,多目標(biāo)進(jìn)化算法已經(jīng)被廣泛地應(yīng)用在社會的各個(gè)領(lǐng)域,正在深刻的改變科學(xué)研究和生產(chǎn)實(shí)踐應(yīng)用等的方方面面。然而,多目標(biāo)進(jìn)化算法薄弱而滯后的數(shù)學(xué)理論研究己經(jīng)嚴(yán)重阻礙了其在計(jì)算智能領(lǐng)域的進(jìn)一步應(yīng)用與發(fā)展。本文對多目標(biāo)進(jìn)化算法的理論、算法設(shè)計(jì)以及相應(yīng)的實(shí)際應(yīng)用進(jìn)行了深入地研究。首先,針對多目標(biāo)優(yōu)化問題中的一類搜索不均衡問題,從理論上分析了造成搜索不均衡問題的原因,分析并定義了三類主要的搜索不均衡問題。針對這類搜索不均衡的問題設(shè)計(jì)出了一類基于種群分解的算法,并通過一系列的數(shù)值仿真實(shí)驗(yàn)驗(yàn)證了所提出算法的有效性。其次,本文研究了基于分解的進(jìn)化多目標(biāo)優(yōu)化算法中內(nèi)在并行性的外在控制理論。在此理論的基礎(chǔ)上,著重研究...
【文章來源】：廣東工業(yè)大學(xué)廣東省
【文章頁數(shù)】：303 頁
【學(xué)位級別】：博士
【文章目錄】：
ABSTRACT
摘要
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 Di-versity 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 Evo-lutionary Many-Objective Optimization Algorithms
3.1 Overview
3.2 Introduction
3.3 Preliminaries
3.3.1 MOEA/D Framework
3.3.2 NSGA-Ⅲ Framework
3.4 Explicit Control of Implicit Parallelism
3.5 Variants of N2M 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 Normalization
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 Algorithms
3.9 Conclusions
Chapter 4 Effect of Objective Normalization and Penalty Parameter on PBI De-composition Based Evolutionary Many-objective Optimization Algo-rithms
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
4.5.2 Parameter Settings
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 DecompositionBased 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 Allocation for Many-objective Optimization
6.1 Overview
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
6.4.1 Adaptive Subregion Division
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 (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
7.5.3 General Parameter Settings
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 Evolution-ary Multi-objective Algorithm
8.1 Overview
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
8.5.1 Encoding Method
8.5.2 Decoding Method
8.5.3 Initialization Based on Fuzzy Clustering
8.5.4 Crossover and Mutation
8.5.5 Constraint Handling and Repair Strategy
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
9.1 Overview
9.2 Introduction
9.3 Preliminaries
9.3.1 Microarray and time-series gene expression data
9.3.2 Triclustering
9.4 Multi-objective constrained triclustering
9.5 Decomposition based evolutionary algorithm for multi-objective constrained 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 Key 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
Acknowledgements
本文編號：3322159
【文章來源】：廣東工業(yè)大學(xué)廣東省
【文章頁數(shù)】：303 頁
【學(xué)位級別】：博士
【文章目錄】：
ABSTRACT
摘要
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 Di-versity 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 Evo-lutionary Many-Objective Optimization Algorithms
3.1 Overview
3.2 Introduction
3.3 Preliminaries
3.3.1 MOEA/D Framework
3.3.2 NSGA-Ⅲ Framework
3.4 Explicit Control of Implicit Parallelism
3.5 Variants of N2M 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 Normalization
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 Algorithms
3.9 Conclusions
Chapter 4 Effect of Objective Normalization and Penalty Parameter on PBI De-composition Based Evolutionary Many-objective Optimization Algo-rithms
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
4.5.2 Parameter Settings
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 DecompositionBased 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 Allocation for Many-objective Optimization
6.1 Overview
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
6.4.1 Adaptive Subregion Division
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 (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
7.5.3 General Parameter Settings
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 Evolution-ary Multi-objective Algorithm
8.1 Overview
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
8.5.1 Encoding Method
8.5.2 Decoding Method
8.5.3 Initialization Based on Fuzzy Clustering
8.5.4 Crossover and Mutation
8.5.5 Constraint Handling and Repair Strategy
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
9.1 Overview
9.2 Introduction
9.3 Preliminaries
9.3.1 Microarray and time-series gene expression data
9.3.2 Triclustering
9.4 Multi-objective constrained triclustering
9.5 Decomposition based evolutionary algorithm for multi-objective constrained 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 Key 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
Acknowledgements
本文編號：3322159
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