Evolutionary Multi-Criterion Optimization

Name: Evolutionary Multi-Criterion Optimization | 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings
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10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings

Kalyanmoy Deb Erik Goodman Carlos A. Coello Coello Kathrin Klamroth Kaisa Miettinen Sanaz Mostaghim Patrick Reed(Editor)

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Published on 28. February 2019

XX, 757 pages

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Content

Intro
Preface
Organization
Contents
Theory
On Bi-objective Convex-Quadratic Problems
1 Introduction
2 Theoretical Properties of Bi-objective Convex-Quadratic Problems
2.1 Preliminaries
2.2 Pareto Set
2.3 Convexity of the Pareto Front
3 New Classes of Bi-objective Test Functions
4 Summary
References
An Empirical Investigation of the Optimality and Monotonicity Properties of Multiobjective Archiving Methods
1 Introduction
2 Experimental Design
2.1 Assessment Indexes
2.2 Archivers Investigated
2.3 Test Problems
2.4 General Experimental Settings
3 Results
3.1 Optimal Ratio
3.2 Deterioration Ratio
3.3 Summary
4 Concluding Remarks
References
Evolutionary Multi-objective Optimization Using Benson's Karush-Kuhn-Tucker Proximity Measure
1 Introduction
2 KKT Based Proximity Measure
3 Proposed B-KKT Proximity Measure
4 Results
4.1 Two-Objective Optimization Problems
4.2 Three-Objective Optimization Problems
4.3 Many-Objective Optimization Problems
4.4 Engineering Design Problem
5 Conclusions
References
On the Convergence of Decomposition Algorithms in Many-Objective Problems
1 Introduction
2 Numerical Experiments
3 Interpretation of Results
4 Conclusion
References
Algorithms
A New Hybrid Metaheuristic for Equality Constrained Bi-objective Optimization Problems
1 Introduction
2 Background
3 Proposed Algorithm (M-NSGA-II/PT)
3.1 First Stage: Rough Approximation via Micro-NSGA-II
3.2 Second Stage: Refinement via PT
4 Numerical Results
5 Conclusions and Future Work
References
Make Evolutionary Multiobjective Algorithms Scale Better with Advanced Data Structures: Van Emde Boas Tree for Non-dominated Sorting
1 Introduction
2 Preliminaries
3 The Divide-and-Conquer Algorithm for Non-dominated Sorting
3.1 The General Plan
3.2 Sweep Line Algorithms for m = 2
4 The Van Emde Boas Tree
5 Efficient Implementation of the Van Emde Boas Tree
6 Implementation and Analysis of the Whole Algorithm
7 Experiments
8 Conclusion
References
Toward a New Family of Hybrid Evolutionary Algorithms
1 Introduction
2 Background
3 Subspace Polynomial Mutation Operator
4 Multi-objective Descent Directions Within MOEAs
4.1 Equality Constrained MOPs
4.2 Gradient-Free Descent Direction
5 Application: Hybrid Algorithm for Constrained Optimization
6 Conclusions and Future Work
References
Adjustment of Weight Vectors of Penalty-Based Boundary Intersection Method in MOEA/D
1 Introduction
2 Related Works
3 MOEA/D-PBI with Adjusted Weight Vectors
4 Computational Experiments
4.1 Experimental Settings
4.2 Experimental Results
5 Conclusions
References
GDE4: The Generalized Differential Evolution with Ordered Mutation
1 Introduction
2 Background Review
2.1 Generalized Differential Evolution
2.2 Existing Single Objective Differential Evolution with Ordered Mutation
3 Proposed Algorithm: The Generalized Differential Evolution with the Ordered Mutation (GDE4)
4 Experiment
5 Conclusion Remarks
References
Multi-objective Techniques for Single-Objective Local Search: A Case Study on Traveling Salesman Problem
1 Introduction
2 Multi-objective Optimization
3 Traveling Salesman Problem
3.1 Iterated Local Search for TSP
3.2 TSP Decomposition
4 Non-Dominance Search
4.1 The Idea
4.2 Improving ILS by NDS
5 Experimental Study
6 Conclusion
References
Multimodality in Multi-objective Optimization - More Boon than Bane?
1 Introduction
2 Related Work
3 A Gradient-Based Methodology for Visualizing Multi-objective Landscapes
4 Exploiting Multimodality for Efficient Optimization
5 Discussion and Conclusion
References
Solving Nonlinear Equation Systems Using Multiobjective Differential Evolution
1 Introduction
2 Preliminary Knowledge
2.1 Multiobjective Optimization Techniques
2.2 Differential Evolution
3 The Proposed BiTDE
3.1 Bi-objective Transformation
3.2 Solving the Transformed Problem
4 Experiments
4.1 Evaluation Criterion
4.2 Experimental Setup
4.3 Experimental Results and Comparisons
4.4 Further Study
5 Conclusion
References
Process-Monitoring-for-Quality-A Model Selection Criterion for Genetic Programming
1 Introduction
2 Theoretical Background
2.1 Genetic Programming Algorithm - Model Development Process
2.2 A Primer on Genetic Programming
2.3 Objective 1: Maximum Probability of Correct Decision
2.4 Objective 2: Model Complexity
2.5 Input, Output, and Classification
3 Separability Index
4 The MS Criterion
4.1 Euclidean Distance
4.2 Attributes
4.3 The MS Process
5 Conclusions
References
Evolutionary Many-Constraint Optimization: An Exploratory Analysis
1 Introduction
2 MCOP: Formulation and Motivation
3 MCOP: Methodology
3.1 Constraint Ranking
3.2 Cascaded Constraint-Handling Framework
4 Experiments
4.1 Test Problem and Algorithm Parameters
4.2 Experimental Results
5 Conclusion
References
Many-Objective EMO
Generating Uniformly Distributed Points on a Unit Simplex for Evolutionary Many-Objective Optimization
1 Introduction
1.1 Motivation
2 Filling Methods
2.1 Methods Based on Design of Experiments
2.2 Space Filling Methods
2.3 Structured Filling Methods
2.4 Probabilistic Filling Methods
3 Construction Methods
3.1 Maximally Sparse Creation Method (MSC)
4 Elimination-based Methods
4.1 Maximally Sparse Selection Methods (MSS)
4.2 Reductive Methods (RED)
5 Results
6 Conclusions
References
On Timing the Nadir-Point Estimation and/or Termination of Reference-Based Multi- and Many-objective Evolutionary Algorithms
1 Introduction
2 Related Work on MâOEA Termination Algorithms
3 Proposed Termination Algorithm
4 Integration with NSGA-III
5 Experimental Settings and Results
5.1 Results on Final Termination of NSGA-III
5.2 Results on Timed Nadir-Point Determination by NSGA-III
6 Conclusion
References
Variation Rate: An Alternative to Maintain Diversity in Decision Space for Multi-objective Evolutionary Algorithms
1 Introduction
2 Background and Related Work
3 Proposed Algorithm
4 Numerical Results
5 Conclusions and Future Work
References
Indicator-Based Weight Adaptation for Solving Many-Objective Optimization Problems
1 Introduction
2 Background
2.1 Decomposition
2.2 Addition of Weight Vectors
2.3 Density Estimation
3 Proposed Indicator
4 Experimental Design
5 Experimental Results
6 Conclusion
References
Investigating the Normalization Procedure of NSGA-III
1 Introduction
2 Related Studies
3
4 Normalization Procedure
4.1 Maximum of Non-dominated Front (MNDF)
4.2 Maximum of Extreme Points (ME)
4.3 Revised Hyperplane Through Extreme Points (HYP)
5 Results
6 Conclusions
References
MAC: Many-objective Automatic Algorithm Configuration
1 Introduction
2 Related Works
3 Automatic Algorithm Configuration
4 The Proposed Method
4.1 Initial Design
4.2 Approximation Model
4.3 Exploration
4.4 Exploitation
5 Experimental Results
6 Conclusion
References
A Parallel Tabu Search Heuristic to Approximate Uniform Designs for Reference Set Based MOEAs
1 Introduction
2 Weight Vector Designs for Reference-Based MOEAs
2.1 Mixture Design
2.2 Weight Vector Designs Based on Discrepancy Functions
3 Uniform Design: Problem Statement
3.1 Good Lattice Point
3.2 CD2 Discrepancy Function and Optimization Problem for UD
4 Parallel Tabu Search Based Heuristic for UD Generation
4.1 Specific Features
4.2 General Algorithm
5 Computational Experiments and Results
5.1 Uniform Designs with the Tabu Search Based Heuristic
5.2 Experiments on Classical MOPs
6 Conclusions
References
Comparison of Reference- and Hypervolume-Based MOEA on Solving Many-Objective Optimization Problems
1 Introduction
2 Pre-experimental Phase
2.1 MOEA/D
2.2 NSGA-III
2.3 SMS-EMOA and GSMS-EMOA
2.4 MO-CMA-ES
3 Numerical Experiments
4 Results
4.1 Success Rate
4.2 Evaluation to Convergence
5 Analysis
5.1 Success Rate
5.2 Convergence Speed
6 Conclusion and Future Works
References
Diversity over Dominance Approach for Many-Objective Optimization on Reference-Points-Based Framework
1 Introduction
2 Challenges with Dominance-Based Environmental Selection
3 DoD Approach and Its Implementation
4 Results and Discussion
5 Conclusions
References
A Two-Stage Evolutionary Algorithm for Many-Objective Optimization
1 Introduction
2 Related Works
2.1 NSGA-iii
2.2 MOEA/DD
3 Proposed Algorithm: TSMOEA
3.1 Framework of Proposed Algorithm
3.2 A Variant of NSGA-iii
3.3 Switching from 1st Stage to 2nd Stage
3.4 Weight Vector Adaptation Strategy
4 Experimental Results
4.1 Setting
4.2 Performance Comparisons on DTLZ Test Suite
4.3 Performance Comparisons on WFG Test Suite
4.4 The Impact of the Length of the Two Stages
5 Conclusion
References
Performance Metrics and Indicators
CRI-EMOA: A Pareto-Front Shape Invariant Evolutionary Multi-objective Algorithm
1 Introduction
2 Background
3 Our Proposed Approach
4 Experimental Results
4.1 Parameters Settings
4.2 Discussion of Results
5 Conclusions and Future Work
References
The Hypervolume Indicator as a Performance Measure in Dynamic Optimization
1 Introduction
2 Dynamic Optimization: Background and Measures
3 Assessing the Anytime Behavior of a Dynamic Optimizer: The Hypervolume Approach
4 Experimental Study
4.1 Problem and Algorithm Definitions
4.2 Experimental Setup
4.3 Results
5 Conclusions
References
Comparison of Hypervolume, IGD and IGD+ from the Viewpoint of Optimal Distributions of Solutions
Abstract
1 Introduction
2 IGD and IGD+ Indicators
3 Optimal Distributions for Two-Objective Problems
4 Optimal Distributions for Three-Objective Problems
5 Conclusions
Acknowledgments
References
Diversity-Indicator Based Multi-Objective Evolutionary Algorithm: DI-MOEA
1 Introduction
2 Diversity Indicators and Gap Contribution
3 Proposed Algorithm
4 Experimental Results and Discussion
4.1 Experimental Setup
4.2 Experiments on Bi-objective Problems
4.3 Experiments on Three Objective Problems
5 Conclusions and Further Work
References
The Expected R2-Indicator Improvement for Multi-objective Bayesian Optimization
1 Introduction
2 The R2 Indicator and Its Expected Improvement
2.1 Expected R2-Indicator Improvement
3 Computation of the Expected R2-Indicator Improvement
4 Numerical Results
5 Conclusions and Outlook
References
Innovization and Surrogates
Trust-Region Based Multi-objective Optimization for Low Budget Scenarios
1 Introduction
2 Related Studies
3 Trust Region Method for Single-Objective Optimization
3.1 Challenges and Motivation for Multi-objective Optimization
4 Proposed Trust Region in Metamodel-Based Multi-objective Evolutionary Algorithm
4.1 Proposed Trust Region Concept
4.2 Performance Indicators for Updating Trust Radius
4.3 Overall Trust Region Adaptation
5 Proposed Overall Algorithm
6 Results
6.1 Two-Objective Unconstrained Problems
6.2 Two-Objective Constrained Problems
6.3 Three-Objective and Real-World Problems
6.4 Dynamics of Trust Region Adaptation
7 Conclusions
References
Approximating Pareto Set Topology by Cubic Interpolation on Bi-objective Problems
1 Introduction
2 Difficult PS Topology
3 Interpolation of PS Topology
4 Experimental Setup
5 Experimental Results and Discussion
6 Conclusion
References
Linear Search Mechanism for Multi- and Many-Objective Optimisation
1 Introduction
2 Multi-objective Optimisation and Related Work
3 Proposed Approach
3.1 Concept
3.2 Inclusion into Other Algorithms
4 Evaluation
4.1 Parameter Settings
4.2 Results
5 Conclusion and Future Work
References
Estimating Relevance of Variables for Effective Recombination
1 Introduction
2 Method
3 Test Problems
4 Experimental Setup and Performance Measures
5 Simulation Results and Discussion
5.1 Three-Objective Benchmark Problems
5.2 Vibrating Beam Problem
6 Conclusions
References
sParEGO - A Hybrid Optimization Algorithm for Expensive Uncertain Multi-objective Optimization Problems
1 Introduction
2 Threshold-Based Robustness Metric
3 The Framework of the sParEGO Algorithm
3.1 Decomposition
3.2 Initialisation
3.3 Uncertainty Quantification
3.4 Estimating the Robustness Indicator Value
3.5 Fitting a Surrogate Model to the Fitness
4 Hypothesis Testing
5 Experimental Results
5.1 Experimental Settings
5.2 Findings
6 Conclusion
References
Knowledge Discovery in Scheduling Systems Using Evolutionary Bilevel Optimization and Visual Analytics
Abstract
1 Introduction
2 Bilevel Innovization
2.1 Visual Analytics and Knowledge Discovery
2.2 Bilevel Innovization Process
3 Problem Description
3.1 Planning Problem
3.2 Scheduling Problem
4 Algorithmic Approach
5 Experimental Study
5.1 Problem Setting
5.2 Data Generation
5.3 Data Analysis
6 Conclusions and Future Research
References
Pareto Optimal Set Approximation by Models: A Linear Case
1 Introduction
2 Local Linear Model
3 Algorithm Framework
4 Experimental Study
4.1 Comparison Study
4.2 Sensitivity to Control Parameters
5 Conclusion
References
On Dealing with Uncertainties from Kriging Models in Offline Data-Driven Evolutionary Multiobjective Optimization
1 Introduction
2 Background
2.1 Generic Offline Data-Driven EMO
2.2 Kriging
3 Approaches to Incorporate Uncertainty
4 Experimental Results
5 Conclusions
References
Convergence Acceleration for Multiobjective Sparse Reconstruction via Knowledge Transfer
1 Introduction
2 Preliminaries
2.1 Maximum Mean Discrepancy
2.2 Marginalized Denoising Autoencoder
3 MOSR via Transfer Operator
3.1 Framework
3.2 Sparse-Constraint Knowledge Transfer Operator
4 Experiments and Discussions
4.1 Experimental Settings
4.2 Experimental Results and Discussions
5 Conclusion
References
Combinatorial EMO
Evolving Generalized Solutions for Robust Multi-objective Optimization: Transportation Analysis in Disaster
1 Introduction
2 Generalization-Based MOEA
2.1 Learning Classifier System and Its Generalization
2.2 Swap-Based Generalization
2.3 Swap-Based Generalization Mechanism
2.4 Fitness Distance
2.5 Evaluation of Generalized Individual
2.6 Algorithm of Generalization-Based MOEA (G-MOEA)
3 Waterbus Route Optimization Problem
3.1 Problem Description
3.2 Evaluation Criteria
4 Experiment
4.1 Waterbus Route Optimization Problem
4.2 Experimental Results
4.3 Discussion
5 Conclusions
References
Runtime Analysis of Evolutionary Multi-objective Algorithms Optimising the Degree and Diameter of Spanning Trees
1 Introduction
2 Preliminaries
3 The Max-Max Problem
4 The Min-Min Problems
5 Conclusions
References
Bi-objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm
1 Introduction
2 Background and Related Work
2.1 Static Multi-objective Optimization Problems
2.2 The Dynamic Multi-objective Vehicle Routing Problem
3 The Dynamic Multi-objective Evolutionary Algorithm
4 Computational Experiments
5 Conclusions and Outlook
References
A Formal Model for Multi-objective Optimisation of Network Function Virtualisation Placement
1 Introduction
2 Problem Formulation and Model Building
3 Instantiation of NFV Placement Problem
3.1 Fat Tree Networks
3.2 Optimisation Algorithm
4 Proof-of-Concept Experiments
5 Conclusion
References
NSGA-II for Solving Multiobjective Integer Minimum Cost Flow Problem with Probabilistic Tree-Based Representation
1 Introduction
2 Problem Formulation
3 NSGA-II and the Probabilistic Tree-Based Representation for MCFP
3.1 Representation
3.2 NSGA-II for Solving MOIMCFP
4 Computational Results
4.1 Test Instances
4.2 Results and Analysis
5 Conclusion
References
Opposition-Based Multi-objective Binary Differential Evolution for Multi-label Feature Selection
1 Introduction
2 Background Review
2.1 Generalized Differential Evolution (GDE3)
2.2 Objective Functions for Multi-label Feature Selection
3 Binary GDE3 for Multi-label Feature Selection
4 Experiments
4.1 Datasets and Settings
4.2 Results and Discussion
5 Conclusion and Future Work
References
Configuration of a Dynamic MOLS Algorithm for Bi-objective Flowshop Scheduling
1 Introduction
2 Automatic Design of a Dynamic Algorithm
2.1 Static vs Dynamic Design Approaches
2.2 A Dynamic Algorithm Framework
2.3 Automatic Configuration of Our Framework
2.4 Related Work
3 Multi-objective Local Search
3.1 The MOLS Framework
3.2 MOLS Component Strategies
4 Experimental Setup
5 Experimental Results
5.1 Evaluation of Dynamic MOLS
5.2 Performance of the Dynamic vs Static MOLS
6 Conclusions and Future Work
References
MCDM and Interactive EMO
Reliable Biobjective Solution of Stochastic Problems Using Metamodels
1 Introduction
2 Concepts and Methods
2.1 Kriging
2.2 Pareto Frontier and Pareto Set
2.3 Attainment Function
3 Approaches for Estimating Expectation and Standard Deviation of f
3.1 General Idea
3.2 Description of Our Approach
3.3 Description of the Classic Method
4 Experiments
4.1 Design of Experiments
4.2 Computational Aspects
5 Evaluation of the Experiments
5.1 Pareto Frontiers
5.2 Pareto Sets
6 Conclusion
References
Mutual Rationalizability in Vector-Payoff Games
Abstract
1 Introduction
2 The Game and the Proposed MPMR Solution Concept
2.1 Problem Formulation
2.2 The Proposed Solution Concept
2.3 Irrational Strategies and Set Domination
3 TSP-MOG Example
4 Aspects of Algorithm Development
5 Summary and Conclusions
Acknowledgment
References
Trend Mining: A Visualization Technique to Discover Variable Trends in the Objective Space
1 Introduction
1.1 A Brief Summary of MCDM Methods
1.2 Decision Making and Decision Space
2 Visualization in MCDM
3 Trend Mining Procedure
3.1 Creation of Reference Points and Reference Vectors
3.2 Projection of Solutions onto Reference Vectors
3.3 Generation of Variable Trend Lines
3.4 Calculation of Interestingness Scores
3.5 Heatmap Visualization of Interestingness Scores
4 Results and Discussion
5 Conclusions
References
IRA-EMO: Interactive Method Using Reservation and Aspiration Levels for Evolutionary Multiobjective Optimization
1 Introduction
2 Background
3 IRA-EMO for Decision Making
3.1 Modified WASF-GA
3.2 Algorithm of IRA-EMO
4 Numerical Example
5 Conclusions
References
Progressive Preference Learning: Proof-of-Principle Results in MOEA/D
1 Introduction
2 Proposed Method
2.1 Consultation Module
2.2 Preference Elicitation Module
3 Experimental Settings
4 Empirical Results
5 Conclusions
References
A Dichotomous Approach to Reduce Rank Reversal Occurrences in PROMETHEE II Rankings
1 Introduction
2 Promethee II
3 Dichotomous Method
4 Comparison of the Two Methods
5 Rank Reversal
6 Conclusion
References
A Viability Study of Renewables and Energy Storage Systems Using Multicriteria Decision Making and an Evolutionary Approach
1 Introduction
2 The HMGS Model
3 Decision Making Methods: AHP+TOPSIS
4 Life Cycle Assessment
5 Experiments and Results
5.1 LCA Results for Global Warming Potential
5.2 MCDM Results and Discussions
6 Conclusion
References
Applications
Analysing Optimisation Data for Multicriteria Building Spatial Design
1 Introduction
2 Problem
3 Features
4 Data Preparation
5 Results
5.1 Box Plots
5.2 Decision Trees
6 Conclusion
References
Constrained Multi-objective Optimization Method for Practical Scientific Workflow Resource Selection
Abstract
1 Introduction
2 The Feasible Configurations Generation Process
3 Two Alternative Feasible Pareto-Optimal Solutions Selection Techniques
3.1 Technique 1: Nondominated Sorting Equivalent Transformation (NSET)
3.2 Technique 2: Feasible Solutions Common to NSGA-II/III Results
4 Experimental Evaluation
4.1 Workflow and Parameters
4.2 Evaluation Results
5 Conclusion
Acknowledgment
References
Simulation Optimization of Water Usage and Crop Yield Using Precision Irrigation
1 Introduction
2 Two Simulation Software: HYDRUS-2D and DSSAT
3 Calibration of Simulation Models
4 Optimization of Water Use Efficiency and Crop Yield
4.1 A Computationally Fast Approach
4.2 Optimization of Crop Production and Water Use
5 Experimental Results
5.1 Calibration and Validation Results
5.2 Bi-objective Optimization Results
6 Conclusions and Future Work
References
Optimum Wind Farm Layouts: A Many-Objective Perspective and Case Study
1 Introduction
2 Wind Farm Layout Optimization Problem
2.1 Generic Problem Formulation
2.2 Solution Representation
2.3 Objectives and Computation Models
2.4 Case Study Description
3 Algorithm
4 Results
5 Conclusions and Future Work
References
Comparison of Multi-objective Optimization Methods Applied to Electrical Machine Design
1 Introduction
2 Problem Definition
3 LPMSM Model
4 Optimization Algorithm
4.1 Procedure
4.2 Evaluation Metrics
5 Optimization Results
6 Conclusion
References
Designing Solar Chimney Power Plant Using Meta-modeling, Multi-objective Optimization, and Innovization
1 Introduction
2 Structure of Solar Chimney Power Plant
3 Proposed Framework
3.1 Meta-modeling Using Genetic Programming
3.2 Multi-objective Optimization: Maximization of Output Power and Efficiency
3.3 Multi Objective Optimization
4 Experimental Results and Analysis
4.1 Genetic Programming Based Innovization
5 Conclusion Remarks
References
Neuroevolutionary Multiobjective Methodology for the Optimization of the Injection Blow Molding Process
Abstract
1 Introduction
2 Injection Blow Molding Optimization
2.1 Process Overview
2.2 Global Optimization
2.3 Proposed Methodology
3 Experiments and Results
3.1 Experimental Setup
3.2 Optimization Results
4 Conclusions
Acknowledgements
References
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Evolutionary Multi-Criterion Optimization

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