
Evolutionary Multi-Criterion Optimization
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The 59 revised full papers were carefully reviewed and selected from 76 submissions. The papers are divided into 8 categories, each representing a key area of current interest in the EMO ?eld today. They include theoretical developments, algorithmic developments, issues in many-objective optimization, performance metrics, knowledge extraction and surrogate-based EMO, multi-objective combinatorial problem solving, MCDM and interactive EMO methods, and applications.
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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
- Author Index
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