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MultiJuMP

MultiJuMP enables the user to easily run multiobjective optimisation problems and generate Pareto fronts. The code is built as an extension of JuMP. We have implemented three ways to trace out the Pareto front:

• Normal Boundary Intersection (`optimize!(m, method = NBI())`)
• This method is applicable only for nonlinear optimisation
• Weighted sums (`optimize!(m, method = WeightedSum())`)
• Constraint methods (`optimize!(m, method = EpsilonCons())`)
• This method only works for biobjective optimisation as of now

Disclaimer: MultiJuMP is not developed or maintained by the JuMP developers.

Installation

In Julia, call `Pkg.add("MultiJuMP")` to install MultiJuMP.

Usage

Have a look at the tests and `examples/` directory for different use cases, including tri-objective Pareto fronts.

MultiJuMP supports linear problems using the `linear=true` keyword when calling `multi_model(linear=true)`. Currently, only the `EpsilonCons()` and `WeightedSum()` methods are supported for linear problems.

``````using MultiJuMP, JuMP
using Clp

const mmodel = multi_model(Clp.Optimizer, linear = true)
const y = @variable(mmodel, 0 <= y <= 10.0)
const z = @variable(mmodel, 0 <= z <= 10.0)
@constraint(mmodel, y + z <= 15.0)

# objectives
const exp_obj1 = @expression(mmodel, -y +0.05 * z)
const exp_obj2 = @expression(mmodel, 0.05 * y - z)
const obj1 = SingleObjective(exp_obj1)
const obj2 = SingleObjective(exp_obj2)

# setting objectives in the data
const multim = get_multidata(mmodel)
multim.objectives = [obj1, obj2]

optimize!(mmodel, method = WeightedSum())

# Get the Utopia and Nadir points
utopiapoint = getutopia(multim)
``````

Plotting with `Plots.jl` is supported via recipes:

``````using Plots: plot, title!, scatter!
pltlin = plot(multim)
title!(pltlin, "Extrema of the Pareto front")

# Show Utopia and Nadir points
# (This is probably a hacky way to do this)
scatter!(pltlin,
``````

As a non-linear usage example, we implement the test from Das and Dennis, 1998: Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems:

``````using MultiJuMP, JuMP
using Ipopt

m = multi_model(Ipopt.Optimizer)
@variable(m, x[i=1:5])
@NLexpression(m, f1, sum(x[i]^2 for i=1:5))
@NLexpression(m, f2, 3x[1]+2x[2]-x[3]/3+0.01*(x[4]-x[5])^3)
@NLconstraint(m, x[1]+2x[2]-x[3]-0.5x[4]+x[5]==2)
@NLconstraint(m, 4x[1]-2x[2]+0.8x[3]+0.6x[4]+0.5x[5]^2 == 0)
@NLconstraint(m, sum(x[i]^2 for i=1:5) <= 10)

iv1 = [0.3, 0.5, -0.26, -0.13, 0.28] # Initial guess
obj1 = SingleObjective(f1, sense = MOI.MIN_SENSE,
iv = Dict{String,Any}("x[\$i]" => iv1[i] for i in 1:length(iv1)))
obj2 = SingleObjective(f2, sense = MOI.MIN_SENSE)

md = get_multidata(m)
md.objectives = [obj1, obj2]
md.pointsperdim = 20
optimize!(m, method = NBI(false)) # or method = WeightedSum() or method = EpsilonCons()

# Get the Utopia and Nadir points
utopiapoint = getutopia(md)

using Plots
pltnbi = plot(md)
``````

09/03/2015

3 months ago

85 commits