odow / MOO.jl

MOO: the Multi-Objective Optimizer

MOO is a collection of algorithms for multi-objective optimization.

Installation

This package is currently under development. You can install it as follows:

] add MathOptInterface#od/vector-optimization
] add JuMP#od/vector-optimization
] add https://github.com/odow/MOO.jl

Usage with JuMP

Use MOO with JuMP as follows:

using JuMP
import HiGHS, MOO
model = JuMP.Model(() -> MOO.Optimizer(HiGHS.Optimizer))
set_optimizer_attribute(model, MOO.Algorithm(), MOO.NISE())
set_optimizer_attribute(model, MOO.SolutionLimit(), 4)

Replace HiGHS.Optimizer with an optimizer capable of solving a single-objective instance of your optimization problem.

You must set the MOO.Algorithm attribute to choose the solution algorithm.

You may set additional optimizer attributes, the supported attributes depend on the choice of solution algorithm.

Algorithm

There are a number of algorithms supported by the algorithms in MOO.

• MOO.NISE()
• MOO.Hierarchical()

Consult their docstrings for details.

Other optimizer attributes

There are a number of optimizer attributes supported by the algorithms in MOO.

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.

• MOO.SolutionLimit()
• MOO.ObjectivePriority(index::Int)
• MOO.ObjectiveWeight(index::Int)
• MOO.ObjectiveRelativeTolerance(index::Int)