Evolutionary Multi-Criterion Optimization
6th International Conference, EMO 2011, Ouro Preto, Brazil, April 5-8, 2011, Proceedings
Published on 25. March 2011
XV, 620 pages
978-3-642-19893-9 (ISBN)
Description
This book constitutes the refereed proceedings of the 6th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2011, held in Ouro Preto, Brazil, in April 2011. The 42 revised full papers presented were carefully reviewed and selected from 83 submissions. The papers deal with fundamental questions of EMO theory, such as the development of algorithmically efficient tools for the evaluation of solution-set quality , the theoretical questions related to solution archiving and others. They report on the continuing effort in the development of algorithms, either for dealing with particular classes of problems or for new forms of processing the problem information. Almost one third of the papers is related to EMO applications in a diversity of fields. Eleven papers are devoted to promote the interaction with the related field of Multi-Criterion Decision Making (MCDM).
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Ricardo H.C. Takahashi | Kalyanmoy Deb | Elizabeth F. Wanner
Evolutionary Multi-Criterion Optimization
6th International Conference, EMO 2011, Ouro Preto, Brazil, April 5-8, 2011, Proceedings
Book
03/2011
1st Edition
Springer
€106.99
Shipment within 7-9 days
Content
- Title
- Preface
- Organization
- Table of Contents
- I Theoretical Issues
- Automated Innovization for Simultaneous Discovery of Multiple Rules in Bi-objective Problems
- Introduction: A Motivating Example
- Related Work
- Discovering Design Rules through Innovization
- Proposed Automated Innovization Approach
- Finding Optimal a$_ij$'s and b$_ij$'s
- One-Dimensional Grid-Based Clustering
- Significance of Design Rules
- Niching for Multiple Design Rules
- Results
- Truss Design Revisited
- Reduced Row-Echelon Form
- Welded Beam Design
- Conclusions and Future Work
- References
- A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms
- Introduction
- Foundations
- Taxonomy
- Formal Framework
- Integration of the State of the Art
- Discussion
- MATLAB Toolbox for Online Stopping Criteria
- Conclusion and Outlook
- References
- Not All Parents Are Equal for MO-CMA-ES
- Introduction
- State of the Art
- MO-CMA-ES
- Generational and Steady State MO-CMA-ES Algorithms
- New Parent Selections for Steady-State MO-CMA-ES
- Tournament Selection
- Multi-Armed Bandit-Inspired Selection
- Defining Rewards
- Discussion
- Experimental Validation
- Experimental Setting
- Result Analysis
- Conclusion and Perspectives
- References
- On Sequential Online Archiving of Objective Vectors
- Introduction
- Preliminaries
- Optimal Approximation Sets of Bounded Size
- Compatibility of Performance Indicators
- Archivers, Convergence and Approximation
- Convergence and Approximation Definitions
- Basic Archiver Pattern
- Unbounded Archive
- Dominating Archive
- Adaptive -Approx Archiving
- Adaptive -Pareto Archiving
- NSGA-II Archiver
- SPEA2 Archiver
- Adaptive Grid Archiving (AGA)
- Hypervolume Archiver AAS
- Multi-level Grid Archiving (MGA)
- Empirical Study
- MGA Addresses Key Weakness of -Archivers
- MGA vs. AAs for Clustered Points
- Fast Degradation of the SPEA2 Archiver
- The NSGA-II Archiver -Deteriorates
- MGA Is Not Negatively Efficiency Preserving
- Conclusions
- References
- On a Stochastic Differential Equation Approach for Multiobjective Optimization up to Pareto-Criticality
- Introduction
- Notation
- Preliminary Definitions and Problem Statement
- Dominating Cones
- The SSW Algorithm
- The SSW-LS Algorithm
- The Multiobjective Golden Section Line Search
- Results of SSW-LS Algorithm
- Results in Many-Objective Problems
- Conclusions
- References
- Pareto Cone -Dominance: Improving Convergence and Diversity in Multiobjective Evolutionary Algorithms
- Introduction
- Pareto -Dominance
- Pareto Cone -Dominance
- Basic Definitions
- Maintaining a Cone -Pareto Front
- Evaluating the Archive Size
- Experiments and Validation of the Proposed Approach
- Analytical Benchmark Problems
- Performance Metrics
- Parameter Settings
- Statistical Design
- Results and Discussion
- Conclusions
- References
- Variable Preference Modeling Using Multi-Objective Evolutionary Algorithms
- Introduction
- Preliminaries and Theoretical Results
- Experimental Study
- Algorithms and Experimental Setup
- Simulation Results
- Conclusions
- References
- On the Computation of the Empirical Attainment Function
- Introduction
- Background
- The EAF Computation Problem
- The Size of the Output Sets
- The Two-Objective Case
- The Three-Objective Case
- More Than Three Objectives
- Time Complexity and Algorithms
- The Two-Objective Case
- The Three-Objective Case
- Concluding Remarks
- References
- Computing Hypervolume Contributions in Low Dimensions: Asymptotically Optimal Algorithm and Complexity Results
- Introduction
- Preliminaries and Related Work
- Complexity Bounds
- Dimension Sweep Algorithm
- Numerical Experiments
- Conclusion and Outlook
- References
- Preference-Driven Co-evolutionary Algorithms Show Promise for Many-Objective Optimisation
- Introduction
- Method
- Overview
- Methods for Ordering Candidate Solutions
- Test Problems, Performance Indicators and Parameter Settings
- Statistical Treatment
- Results
- Attainment Surface Results
- Dominance Rank Results
- Hypervolume Results
- Discussion
- Findings
- Limitations and Areas for Future Research
- Conclusion
- References
- Adaptive Objective Space Partitioning Using Conflict Information for Many-Objective Optimization
- Introduction
- Basic Concepts and Notation
- The Conflict-Based Partitioning Framework
- General Idea of the Partitioning Framework
- Using Conflict Information to Partition the Objective Space
- Automatic Setting of the Partitioning Parameters
- Proportional Assignment of the Resources
- Automatic Transition between Phases
- Experimental Results
- Algorithms, Metrics and Parameter Settings
- Application of the New Adaptive Scheme
- DTLZ5(I,M): Conflict Known a priori
- Knapsack Problem: Unknown Conflict a priori
- Conclusions and Future Work
- References
- Effects of the Existence of Highly Correlated Objectives on the Behavior of MOEA/D
- Introduction
- MOEA/D
- Computational Experiments on Knapsack Problems
- Computational Experiments on Two-Dimensional Problems
- Conclusions
- References
- Improved Random One-Bit Climbers withAdaptive $\varepsilon$-Ranking and Tabu Moves for Many-Objective Optimization
- Introduction
- Multi-objective Optimization and MNK-Landscapes
- Multi-objective Random One-Bit Climbers
- Improvement Strategies for MoRBCs
- Tabu Moves
- Adaptive -Ranking
- Experimental Design
- Performance Metrics
- Non-parametric Statistical Tests
- Test Problems and Parameter Settings
- Results and Discussions
- Effects of Tabu Ages
- Effects of Adaptive -Ranking
- Comparison among Algorithms
- Conclusions and Future Directions
- References
- Framework for Many-Objective Test Problems with Both Simple and Complicated Pareto-Set Shapes
- Introduction
- Brief Background for Evolutionary Many-Objective Optimization
- Framework for Many-Objective Problems with Arbitrarily Prescribed PS Shapes
- Modular Approach to Test Instances
- EMO Algorithms Used and the Parameter Settings
- Experimental Results
- Conclusions
- References
- II Algorithms
- A Preference Based Interactive Evolutionary Algorithm for Multi-objective Optimization: PIE
- Introduction
- Concepts
- The Proposed PIE Algorithm
- Components of the PIE Algorithm
- PIE Algorithm
- The Archive Sets A and AP
- Numerical Example
- Conclusions
- References
- Preference Ranking Schemes in Multi-Objective Evolutionary Algorithms
- Introduction
- Preliminaries and Existing Studies
- Preference Based Ranking Schemes
- Ranking in pNSGA-II and pssNSGA-II
- Ranking in pSPEA2
- Ranking in pSMPSO
- Ranking in pIBEA
- Experimental Study
- Experimental Setup
- Discussion of Results
- Conclusions
- References
- Interactive Multiobjective Mixed-Integer Optimization Using Dominance-Based Rough Set Approach
- Introduction
- Interactive Multiobjective Optimization Guided by Dominance-Based Rough Set Approach (IMO-DRSA)
- The Bound-and-Cut Algorithm
- A Didactic Example
- Conclusions
- References
- Very Large-Scale Neighborhood Search for Solving Multiobjective Combinatorial Optimization Problems
- Introduction
- Very Large-Scale Neighborhood Search
- The Multiobjective Multidimensionnal Knapsack Problem
- The Multiobjective Set Covering Problem
- Two-Phase Pareto Local Search
- Results
- The Multiobjective Multidimensional Knapsack Problem
- The Multiobjective Set Covering Problem
- Conclusion
- References
- Bilevel Multi-objective Optimization Problem Solving Using Progressively Interactive EMO
- Introduction
- Recent Studies
- Multi-objective Bilevel Optimization Problems
- Progressively Interactive Hybrid Bilevel Evolutionary Multi-objective Optimization Algorithm (PI-HBLEMO)
- Step 3: Elicitation of Preference Information and Construction of a Polynomial Value Function
- Termination Criterion
- Modified Domination Principle
- Parameter Setting
- Results
- Problem DS1
- Problem DS2
- Problem DS3
- Problem DS4
- Problem DS5
- Accuracy and DM Calls
- Conclusions
- References
- Multi-objective Phylogenetic Algorithm: Solving Multi-objective Decomposable Deceptive Problems
- Introduction
- Multi-objective Evolutionary Algorithms
- Multi-objective Estimation of Distribution Algorithms
- Multi-objective Phylogenetic Algorithm
- Distance Metric
- Neighbor Joining
- Test Problems and Results
- Future Work
- Conclusions
- References
- Multi-objective Optimization with Joint Probabilistic Modeling of Objectives and Variables
- Introduction
- Joint Modeling of Objectives and Variables
- Selection
- Model Learning
- Model Sampling
- Replacement
- Experiments
- Test Functions
- Experimental Results
- Conclusion and Further Research
- References
- A Bi-objective Based Hybrid Evolutionary-Classical Algorithm for Handling Equality Constraints
- Introduction
- Bi-objective Hybrid Equality Constraint Handling Method
- Proposed Algorithm
- Simulation Results on Standard Test Problems
- Problem g03
- Problem g05
- Problem g11
- Problem g13
- Problem g14
- Problem g15
- Problem g17
- Problem g21
- Conclusions
- References
- A New Memory Based Variable-Length Encoding Genetic Algorithm for Multiobjective Optimization
- Introduction
- Algorithm Memory and Adaptive Neighborhood
- Algorithm Memory
- Adaptive Neighborhood
- Algorithm Operators
- Evaluation Operator
- Crossover Operator
- Mutation Operator
- The MBVL-NSGA2
- Local Search Operator
- Numerical Results
- Comparison Methodology
- Test Problems
- Results
- Conclusions
- References
- A Concentration-Based Artificial Immune Network for Multi-objective Optimization
- Introduction
- The Concentration-Based Immune Network for Multi-objective Optimization
- Representation and Affinity Metrics
- Concentration Model and Suppression
- Cloning and Hypermutation
- Selection and Insertion of Individuals
- Experimental Results
- Benchmark Problems
- Methodology
- Results and Discussion
- Final Comments and Future Steps
- References
- III Applications
- Bi-objective Portfolio Optimization Using a Customized Hybrid NSGA-II Procedure
- Introduction
- Practical Portfolio Optimization
- Difficulties with Classical Methods
- Customized Hybrid NSGA-II Procedure
- Customized Initialization Procedure
- Customized Recombination Operator
- Customized Mutation Operator
- Clustering
- Local Search
- Results
- Portfolio Optimization with No Size Constraint
- Portfolio Optimization for a Fixed d
- Portfolio Optimization for a Given Range of d
- Conclusions
- References
- Aesthetic Design Using Multi-Objective Evolutionary Algorithms
- Introduction
- Digital Design Method
- State of the Art
- Experimental
- Multi-objective Optimization
- Problem Characteristics
- Optimization Strategy
- Example of Application
- Problem to Solve
- Optimization Results
- Conclusions
- References
- Introducing Reference Point Using g-Dominance in Optimum Design Considering Uncertainties: An Application in Structural Engineering
- Introduction
- The Structural Problem
- Deterministic Design
- Design Including Uncertainties
- Deterministic Optimum Design as Reference Point for Robust Optimization
- Test Case
- Results and Discussion
- Conclusions
- References
- Multiobjective Dynamic Optimization of Vaccination Campaigns Using Convex Quadratic Approximation Local Search
- Introduction
- The SIR Model
- Optimization Model
- Convex Quadratic Approximation
- Optimization Engine
- Experiments and Results
- Conclusion
- References
- Adaptive Technique to Solve Multi-objective Feeder Reconfiguration Problem in Real Time Context
- Introduction
- Related Works
- The Multi-objective Problem
- Component Modeling Issues
- Multi-objective Discussion
- Fuzzy State Estimation
- The Implemented Algorithm
- Experimental Results
- Conclusion
- References
- Variable Neighborhood Multiobjective Genetic Algorithm for the Optimization of Routes on IP Networks
- Introduction
- Problem Description and Modeling
- Structure of Multiobjective Genetic Algorithm
- Variable Neighborhood Search
- The Proposed Multiobjective Genetic Algorithm
- Genetic Representation
- Crossover Operators
- Mutation
- Results
- Conclusions and Future Work
- References
- Real-Time Estimation of Optical Flow Based on Optimized Haar Wavelet Features
- Introduction
- Related Work
- Original Method
- Proposed Algorithm
- Haar Features for Index-Based Matching
- Optional Sub-pixel Refinement
- Multi-objective Optimization
- EvolutionaryMulti-objective Optimization
- Mutating solutions
- Experiments
- Setup
- Results
- Discussion
- Conclusion
- References
- Multi-objective Genetic Algorithm Evaluation in Feature Selection
- Introduction
- Feature Selection
- MOGA in Feature Selection
- Experimental Evaluation
- Results
- Discussion
- Conclusion
- References
- A Cultural Algorithm Applied in a Bi-Objective Uncapacitated Facility Location Problem
- Introduction
- Paper Organization
- Overview
- (Evolutionary) Multi-Objective Optimization
- Bi-Objective Uncapacitated Facility Location Problem (BOUFLP)
- Bi-Objective Cultural Algorithms (BOCA)
- Non-dominated Sets: Metrics
- Metrics for Comparing NDSs
- Computational Experiments
- Instances Presentation
- Results and Discussion
- Conclusions and Future Work
- References
- A Bi-objective Iterated Local Search Heuristic with Path-Relinking for the p-Median Problem
- Introduction
- A Bi-objective ILS Heuristic with Path Relinking
- Initial Solutions
- Perturbation
- Local Search
- Acceptance Criterion
- Intensification with Path Relinking
- Parameters of the Algorithm
- The $\varepsilon$-Constraint Algorithm for the BO-p-MP
- Computational Tests
- Problem Instances
- Performance Measures
- Obtained Results
- Conclusions
- References
- A Framework for Locating Logistic Facilities with Multi-Criteria Decision Analysis
- Introduction
- The Logistic Facility Location Problem
- Decision Models for Facility Location Problems
- Traditional Decision Models
- Decision Models Considering Multiple Objectives
- A Framework for Using Multi-Criteria Analysis in Facility Location Problems
- The Traditional Optimisation Model
- Identifying and Measuring Nodal Benefits
- Identifying and Measuring Topological Benefits
- Measuring Preferences for Costs
- Defining the Overall Logistic Value Optimisation Model
- Determining Value Trade-Offs
- Exploring the Solution Layouts
- Conclusions and Directions for Further Research
- References
- Lorenz versus Pareto Dominance in a Single Machine Scheduling Problem with Rejection
- Introduction
- Pareto and Lorenz Dominance Properties
- Two-Phase Method (TPM) Implementation
- Phase One Implementation
- Phase Two Implementation
- Bi-objective Simulated Annealing Algorithm Based on Lorenz-Dominance Properties
- Numerical Experiments
- Parameters Setting
- Numerical Results
- Conclusion
- References
- GRACE: A Generational Randomized ACO for the Multi-objective Shortest Path Problem
- Introduction
- The Multi-objective Shortest Path Problem
- Ant Colony Optimization
- GRACE: A Two-Phase ACO for the MSP
- Phase I: The Quest for Supported Efficient Solutions
- Phase II: The Ant Colony
- Methodology
- Results and Discussion
- Conclusions
- References
- IV MCDM
- Modeling Decision-Maker Preferences through Utility Function Level Sets
- Introduction
- Notation and Problem Statement
- Utility Function Approximation
- Step 1
- Step 2
- Step 3
- An Illustrative Example
- The Number of Alternatives
- Rank Reversal
- Noisy Decision-Maker
- Conclusions
- References
- A MCDM Model for Urban Water Conservation Strategies Adapting Simos Procedure for Evaluating Alternatives Intra-criteria
- Introduction
- Proposed Model
- Revised Simos Procedure - Adapted to Evaluate Alternatives
- SMARTER
- Case Study
- Survey of Alternatives
- Survey of Attributes
- Application of the Revised Simos Procedure to Evaluate Alternatives on Qualitative Attributes
- Results and Analysis by SMARTER Method
- Concluding Remarks
- References
- A Multicriteria Decision Model for a Combined Burn-In and Replacement Policy
- Introduction
- Mixed Distributions
- MAUT
- Multicriteria Decision Model for a Combined Burn-In and Replacement Process
- Case Study
- Conclusion
- References
- Applying a Multicriteria Decision Model So as to Analyse the Consequences of Failures Observed in RCM Methodology
- Introduction
- RCM - Reliability Centered Maintenance
- Failure Consequences
- Multicriteria Decision Aid
- MAUT (Multiattribute Utility Theory)
- Multicriteria Decision Model for Assessing the Consequences of Functional Failures
- Identifying the Dimensions of the Consequences
- Analysing the Consequences
- Probabilistic Modeling
- Defining the Overall Utility Index
- Ranking the Alternatives
- Numerical Application
- Results
- Conclusions
- References
- Supplier Selection Based on the PROMETHEE VI Multicriteria Method
- Introduction
- Supplier Selection
- Group Decision and Multicriteria Method
- Framework for Supplier Selection with Multicriteria Model
- Numerical Application
- Concluding Remarks
- References
- Author Index
