Multi-Objective Optimization in Theory and Practice II: Metaheuristic Algorithms

Name: Multi-Objective Optimization in Theory and Practice II: Metaheuristic Algorithms
Brand: Bentham Science Publishers
Price: 63.67 EUR
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André A. Keller(Autor*in)

Bentham Science Publishers

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Erschienen am 28. März 2019

300 Seiten

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Inhalt

Cover
Title
Biblography
End User License Agreement
Contents
Preface
Acknowledgements
Pareto-Optimal Front Determination
Pareto-Optimal Front Determination
1.1. INTRODUCTION
1.1.1. Heuristic and Metaheuristic Algorithms
1.1.2. History of Metaheuristics
1.1.3. Probabilistic Metaheuristics and Applications
1.1.4. Optimum Design of Framed Structures: A Review of Literature
1.2. Elements of Static Multi-Objective Programming
1.2.1. Problem Formulation
1.2.2. Concept of Dominance
1.2.3. Pareto-Optimality
1.3. Pareto-Optimal Front
1.3.1. Non-Dominated Solutions
1.3.2. Analytical Pareto-Optimal Front
1.3.3. Near Pareto-Optimal Front
1.3.4. Shapes of a Pareto-Optimal Front
1.4. Selection Procedures of Algorithms
1.4.1. Elitist Pareto Criteria
1.4.2. Non-Pareto Criteria
1.4.3. Bi-criterion Evolution
1.4.4. Other Concepts of Dominance
NOTES
REFERENCES
Untitled
Metaheuristic Optimization Algorithms
Metaheuristic Optimization Algorithms
2.1. INTRODUCTION
2.2. Simulated Annealing Algorithm
2.2.1. Annealing Principle and Description
2.2.2. Problem Formulation
2.2.3. Algorithm Description
2.3. Multi-Objective Simulated Annealing
2.3.1. MOSA Algorithms
2.3.2. Test Problems
NOTES
REFERENCESV
Evolutionary Strategy Algorithms
Evolutionary Strategy Algorithms
3.1. INTRODUCTION1
3.2. Principles and Operators
3.2.1. Algorithm for Solving Optimization Problems
3.2.2. Binary and Real-Number Encoding
3.2.3. Genetic Operators
3.3. GA-Based Mathematica® Notebook
3.4. Single-Objective Optimization
3.4.1. SciLab Package for Genetic Algorithm
3.4.2. GA-Based Software Package: GENOCOP III
NOTES
REFERENCESV
Genetic Search Algorithms
Genetic Search Algorithms
4.1. INTRODUCTION
4.2. Niched Pareto Genetic Algorithms (NPGA)
4.3. Non-Dominated Sorting Genetic Algorithm
4.4. Multi-Objective Optimization Test Problems
4.4.1. Unconstrained Optimization Problems
4.4.2. Constrained Optimization Problem
NOTES
REFERENCESV
Evolution Strategy Algorithms
Evolution Strategy Algorithms
5.1. INTRODUCTION
5.2. Differential Evolution Strategy
5.2.1. Principles and Algorithm2
5.2.2. DE Operators
5.3. DE Algorithm for Single-Objective Optimization Problems
5.4. Multi-Objective DE Algorithm
5.4.1. Diversity-Promoting
5.4.2. Performing Elitism
NOTES
REFERENCESV
Swarm Intelligence and Co-Evolutionary Algorithms
Swarm Intelligence and Co-Evolutionary Algorithms
6.1. INTRODUCTION
6.2. Particle Swarm Optimization
6.3. Cooperative Co-Evolutionary Genetic Algorithms
6.4. Competitive Predator-Prey Optimization Model
6.4.1. Principle of PP Algorithm
6.4.2. PP Algorithm
6.4.3. Illustrative Problems
NOTES
REFERENCESV
Decomposition-Based and Hybrid Evolutionary Algorithms
Decomposition-Based and Hybrid Evolutionary Algorithms
7.1. INTRODUCTION
7.2. Decomposition-Based Algorithm
7.2.1. Scalar Decomposition Principle
7.2.2. Decomposition-Based MOEA Algorithm
7.2.3. MOEA Framework Software Package
7.3. Hybrid Evolutionary Algorithms
7.3.1. Hybridization under Lamarckian Strategy
7.3.2. Two-Stage Hybrid Search Method
7.3.3. Multi-Objective Genetic Local Search Algorithm
7.3.4. Archive-Based Hybrid Scatter Search (AbYSS)
NOTES
REFERENCESV
Many-Objective Optimization and Parallel Computation
Many-Objective Optimization and Parallel Computation
8.1. INTRODUCTION
8.2. Many-Objective Optimization
8.2.1. Dominance Extensions for MaOPs
8.2.2. Efficient Optimizers for MaOPs
8.2.3. Test Problem Suites for MaOPs
8.2.4. A Matlab Platform for MaOPs
8.2.5. Test Problem DTLZ#2 with Many Objectives
Design of Test Problems
Design of Test Problems
9.1. INTRODUCTION
9.2. Key Characteristics of Test Functions
9.2.1. Major Complexities
9.2.2. Generating Methods
9.2.3. Test Suites
9.3. Analytical Pareto-Optimal Fronts
9.4. Multi-Objective Optimization with Mathematica®
9.4.1. Interactive Mathematica® Demonstration
9.4.2. Pareto-Optimal Solution
9.4.3. Resolution Process
NOTES
REFERENCESV
Fifty Collected Test Functions
Fifty Collected Test Functions
10.1. INTRODUCTION
10.2. All Fifty Test Problems
10.2.1. Problem Description
10.2.2. Approximated Pareto Fronts Using NSGA-II
10.2.3. Mathematical Problem Formulation
10.3. Selected Test Problems
NOTES
REFERENCESV
Back pages-corrected
List of Abbreviations
List of Journal Abbreviations in the References
List of Symbols
Subject Index
Back Cover

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