Evolutionary Multi-Criterion Optimization

Name: Evolutionary Multi-Criterion Optimization | 6th International Conference, EMO 2011, Ouro Preto, Brazil, April 5-8, 2011, Proceedings
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6th International Conference, EMO 2011, Ouro Preto, Brazil, April 5-8, 2011, Proceedings

Ricardo H.C. Takahashi Kalyanmoy Deb Elizabeth F. Wanner Salvatore Greco(Herausgeber*in)

Springer (Verlag)

Erschienen am 25. März 2011

XV, 620 Seiten

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Inhalt

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

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