https://github.com/jump-dev/multiobjectivealgorithms.jl

https://github.com/jump-dev/multiobjectivealgorithms.jl

algorithm julia jump multi-objective-optimization optimization

Last synced: 3 days ago
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• Host: GitHub
• URL: https://github.com/jump-dev/multiobjectivealgorithms.jl
• Owner: jump-dev
• License: other
• Created: 2019-12-05T23:05:24.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2023-12-29T22:04:37.000Z (about 1 year ago)
• Last Synced: 2024-04-12T04:13:57.901Z (10 months ago)
• Topics: algorithm, julia, jump, multi-objective-optimization, optimization
• Language: Julia
• Homepage:
• Size: 348 KB
• Stars: 56
• Watchers: 8
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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# MultiObjectiveAlgorithms.jl

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[MultiObjectiveAlgorithms.jl](https://github.com/jump-dev/MultiObjectiveAlgorithms.jl)

(MOA) is a collection of algorithms for multi-objective optimization.

## License

`MultiObjectiveAlgorithms.jl` is licensed under the [MPL 2.0 License](https://github.com/jump-dev/MultiObjectiveAlgorithms.jl/blob/master/LICENSE.md).

## Getting help

If you need help, please ask a question on the [JuMP community forum](https://jump.dev/forum).

If you have a reproducible example of a bug, please [open a GitHub issue](https://github.com/jump-dev/MultiObjectiveAlgorithms.jl/issues/new).

## Installation

Install MOA using `Pkg.add`:

```julia

import Pkg

Pkg.add("MultiObjectiveAlgorithms")

```

## Use with JuMP

Use `MultiObjectiveAlgorithms` with JuMP as follows:

```julia

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.

## 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.

## Other optimizer attributes

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()`