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Systems Science
Optimization Theory
1. Foundations of Optimization
2. Mathematical Foundations
3. Unconstrained Optimization
4. Constrained Optimization Theory
5. Linear Programming
6. Nonlinear Programming
7. Integer and Combinatorial Optimization
8. Dynamic Programming
9. Stochastic Optimization
10. Heuristic and Metaheuristic Methods
11. Multi-Objective Optimization
12. Specialized Optimization Topics
13. Applications and Case Studies
14. Computational Aspects and Software
Multi-Objective Optimization
Fundamental Concepts
Pareto Dominance
Pareto Optimality
Pareto Set and Pareto Front
Ideal and Nadir Points
Utopia Point
Scalarization Approaches
Weighted Sum Method
ε-Constraint Method
Goal Programming
Achievement Scalarizing Functions
Reference Point Methods
Interactive Methods
Progressive Articulation of Preferences
NIMBUS Method
Reference Direction Approach
Satisficing Trade-Off Method
Evolutionary Multi-Objective Optimization
NSGA-II (Non-Dominated Sorting Genetic Algorithm)
SPEA2 (Strength Pareto Evolutionary Algorithm)
MOEA/D (Multi-Objective Evolutionary Algorithm based on Decomposition)
Performance Metrics
Hypervolume
Inverted Generational Distance
Spread Metrics
Many-Objective Optimization
Challenges with High-Dimensional Objectives
Dimensionality Reduction Techniques
Preference-Based Approaches
Decision Making
Multi-Criteria Decision Analysis
Preference Elicitation
Trade-Off Analysis
Robust Decision Making
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12. Specialized Optimization Topics