Expand description
MOEA/D (Multi-objective Evolutionary Algorithm based on Decomposition)
Decomposes a multi-objective optimization problem into a set of scalar optimization subproblems using weight vectors and solves them simultaneously in a collaborative manner through neighborhood relationships.
Key features:
- Uniform weight vector generation for decomposition
- Tchebycheff and weighted-sum scalarization approaches
- Neighborhood-based mating and replacement
- Differential evolution operators for offspring generation
- Adaptive neighborhood size
§References
- Zhang & Li, “MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition”, IEEE TEC 2007
Structs§
- MOEAD
- MOEA/D optimizer
Enums§
- Scalarization
Method - Scalarization approach for MOEA/D