MultiObjectiveAlgorithms.jl (MOA) is a collection of algorithms for multi-objective optimization.
MultiObjectiveAlgorithms.jl is licensed under the MPL 2.0 License.
If you need help, please ask a question on the JuMP community forum.
If you have a reproducible example of a bug, please open a GitHub issue.
Install MOA using Pkg.add:
import Pkg
Pkg.add("MultiObjectiveAlgorithms")Use MultiObjectiveAlgorithms with JuMP as follows:
using JuMP
import HiGHS
import MultiObjectiveAlgorithms as MOA
model = JuMP.Model(() -> MOA.Optimizer(HiGHS.Optimizer))
set_attribute(model, MOA.Algorithm(), MOA.Dichotomy())
set_attribute(model, MOA.SolutionLimit(), 4)Replace HiGHS.Optimizer with an optimizer capable of solving a
single-objective instance of your optimization problem.
You may set additional optimizer attributes, the supported attributes depend on the choice of solution algorithm.
Set the algorithm using the MOA.Algorithm() attribute.
The value must be one of the algorithms supported by MOA:
MOA.Chalmet()MOA.Dichotomy()MOA.DominguezRios()MOA.EpsilonConstraint()MOA.Hierarchical()MOA.KirlikSayin()MOA.Lexicographic()[default]MOA.TambyVanderpooten()
Consult their docstrings for details.
There are a number of optimizer attributes supported by the algorithms in MOA.
Each algorithm supports only a subset of the attributes. Consult the algorithm's docstring for details on which attributes it supports, and how it uses them in the solution process.
MOA.EpsilonConstraintStep()MOA.LexicographicAllPermutations()MOA.ObjectiveAbsoluteTolerance(index::Int)MOA.ObjectivePriority(index::Int)MOA.ObjectiveRelativeTolerance(index::Int)MOA.ObjectiveWeight(index::Int)MOA.SolutionLimit()MOI.TimeLimitSec()