Component-Wise MOEA/D Implementation
Box constraints routine
Inverted Generational Distance
Stop criteria for MOEA/D
NULL constraint handling method for MOEA/D
"Penalty" constraint handling method for MOEA/D
"Violation-based Ranking" constraint handling method for MOEA/D
Create population
Problem Decomposition using Multi-layered Simplex-lattice Design
Problem Decomposition using Simplex-lattice Design
Problem Decomposition using Uniform Design
Calculate neighborhood relations
Evaluate population
Example problem
Find non-dominated points
Calculate weight vectors
Print available constraint methods
Print available decomposition methods
Print available local search methods
Print available scalarization methods
Print available stop criteria
Print available update methods
Print available variation operators
Differential vector-based local search
Three-point quadratic approximation local search
Make vectorized smoof function
MOEA/D
Order Neighborhood for MOEA/D
Run variation operators
plot.moead
preset_moead
print.moead
Print progress of MOEA/D
Adjusted Weighted Tchebycheff Scalarization
Inverted Penalty-based Boundary Intersection Scalarization
Penalty-based Boundary Intersection Scalarization
Weighted Sum Scalarization
Weighted Tchebycheff Scalarization
Scalarize values for MOEA/D
Scaling of the objective function values
Stop criterion: maximum number of evaluations
Stop criterion: maximum number of iterations
Stop criterion: maximum runtime
summary.moead
Unitary constraints routine
Update population
Best Neighborhood Replacement Update for MOEA/D
Restricted Neighborhood Replacement Update for MOEA/D
Standard Neighborhood Replacement Update for MOEA/D
Binomial Recombination
Differential Mutation
Local search Operators
Identity operator
Polynomial mutation
Simulated binary crossover
Truncate
Modular implementation of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) [Zhang and Li (2007), <DOI:10.1109/TEVC.2007.892759>] for quick assembling and testing of new algorithmic components, as well as easy replication of published MOEA/D proposals. The full framework is documented in a paper published in the Journal of Statistical Software [<doi:10.18637/jss.v092.i06>].