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blibliboeLaar
blibliboe
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Laar
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32 refractor code for multiple method to generate alternatives (#38)
* Updated the generate parralel to return state aka all solutions from metaheuristics * Updated generate alternatives to make multiple MGA techniques possible * Update optimization specifically to also allow for multiple MGa techniques * Added the max-distance method to the MGA methods, this was already implemented * Added the max-distance method to the MGA methods, this was already implemented * Changed it so that max distance is not neccesarily the standard when testing * Exported core functions * Explicitly used JuMP when calling all variables * Changes based on the formatter * Capitalized the D in Max-Distance * Removed a link that does not work according to the pre-commit * Added test coverage * Moved the test file to the test directory and not in a subdirectory * Removed the unneccessary check for test in the run test file * Fixed a format error --------- Co-authored-by: Laar <luuk.vandelaar@tno.nl>
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‎Project.toml‎

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@@ -6,11 +6,13 @@ version = "0.1.0"
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[deps]
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DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
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Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
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Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
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JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
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MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
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Metaheuristics = "bcdb8e00-2c21-11e9-3065-2b553b22f898"
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[compat]
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Ipopt = "1.7.1"
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DataStructures = "0.19"
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Distances = "0.10"
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JuMP = "1"

‎coverage/lcov.info‎

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SF:src\MGA-Methods\Max-Distance.jl
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DA:29,16
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DA:36,8
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DA:37,8
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DA:40,8
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DA:42,8
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DA:43,8
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DA:71,4
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DA:77,2
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DA:79,2
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DA:80,2
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DA:83,2
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DA:85,2
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LH:12
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LF:12
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end_of_record
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SF:src\NearOptimalAlternatives.jl
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LH:0
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LF:0
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end_of_record
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SF:src\alternative-metaheuristics.jl
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DA:5,7
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DA:26,8
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DA:32,8
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DA:33,8
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DA:34,28
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DA:35,21
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DA:37,1
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DA:39,14
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DA:40,8
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DA:59,91022
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DA:65,91022
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DA:67,91022
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DA:69,182044
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DA:71,364094
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DA:72,172044
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DA:74,10003
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DA:76,273072
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DA:77,91022
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DA:100,9
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DA:108,9
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DA:109,18
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DA:111,9
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DA:118,91015
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DA:120,91006
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DA:131,0
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DA:138,91006
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DA:140,91006
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DA:141,364022
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DA:142,364022
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DA:145,364022
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DA:147,6
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DA:148,2
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DA:150,2
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DA:159,0
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DA:164,364016
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DA:166,728032
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DA:167,20000
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DA:170,344016
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DA:171,172008
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DA:174,0
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DA:186,91006
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DA:312,20003
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DA:317,2
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DA:327,7
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LH:95
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LF:113
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end_of_record
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SF:src\alternative-optimisation.jl
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DA:47,16
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end_of_record
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SF:src\generate-alternatives.jl
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DA:24,16
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DA:34,16
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DA:35,1
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DA:36,7
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DA:37,1
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end_of_record
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SF:src\results.jl
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DA:5,12
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end_of_record

‎src/MGA-Methods/Max-Distance.jl‎

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export Dist_update!, Dist_initial!
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"""
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Dist_initial!(
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model::JuMP.Model,
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variables::AbstractArray{T,N},
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fixed_variables::Vector{VariableRef};
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weights::Vector{Float64} = zeros(length(variables)),
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metric::Distances.SemiMetric = SqEuclidean(),
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) where {T<:Union{VariableRef,AffExpr},N}
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Initialize a JuMP model's objective to maximize the distance between the current solution and a reference solution, based on a specified metric.
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This function is typically used in the context of generating diverse solutions (alternatives) to an optimization problem by first defining a distance-based objective that measures how different a new solution is from an existing (optimal) one.
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# Arguments
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- `model::JuMP.Model`: a JuMP model that has been previously solved.
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- `variables::AbstractArray{T,N}`: the variables of the model to consider in the distance computation.
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- `fixed_variables::Vector{VariableRef}`: a subset of all variables of `model` that are not allowed to be changed when seeking for alternatives.
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- `weights::Vector{Float64}`: optional weights to influence the distance calculation (currently not used directly but reserved for extensions).
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- `metric::Distances.SemiMetric`: the distance metric used to compute dissimilarity (default is squared Euclidean distance).
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# Behavior
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- Extracts the current solution values of `variables`.
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- Sets the model's objective to maximize the distance between the current variable values and the solution.
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- Changes the model's objective sense to `Max`.
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"""
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function Dist_initial!(
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model::JuMP.Model,
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variables::AbstractArray{T,N},
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fixed_variables::Vector{VariableRef};
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weights::Vector{Float64} = zeros(length(variables)),
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metric::Distances.SemiMetric = SqEuclidean(),
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) where {T<:Union{VariableRef,AffExpr},N}
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vars_vec = [v for v in variables]
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solution = value.(vars_vec)
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# Fix the variables that are fixed
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fix.(fixed_variables, value.(fixed_variables), force = true)
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set_objective_sense(model, FEASIBILITY_SENSE)
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@objective(model, Max, Distances.evaluate(metric, vars_vec, solution))
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end
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"""
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Dist_update!(
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model::JuMP.Model,
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variables::AbstractArray{T,N};
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weights::Vector{Float64} = zeros(length(variables)),
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metric::Distances.SemiMetric = Cityblock(),
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) where {T<:Union{VariableRef,AffExpr},N}
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Update a JuMP model's objective function to include an additional distance term for generating multiple diverse alternatives (as in Modeling to Generate Alternatives).
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This function builds upon a previously defined objective by incrementally adding a distance term between the current solution and a new reference solution. It is typically used after `Dist_initial!` or a prior call to `MGA_Dist_update!`.
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# Arguments
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- `model::JuMP.Model`: the JuMP model being updated to generate further alternatives.
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- `variables::AbstractArray{T,N}`: the variables to consider in the distance computation.
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- `weights::Vector{Float64}`: optional weights for the distance metric (currently not directly used).
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- `metric::Distances.SemiMetric`: the distance metric used to compute dissimilarity (default is Cityblock distance).
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# Behavior
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- Evaluates the current objective function to retrieve the cumulative distance so far.
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- Computes the distance between the current variable values and their previous optimal values.
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- Updates the objective function to maximize the sum of the cumulative and new distances.
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- Resets the model's objective sense to `Max`.
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"""
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function Dist_update!(
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model::JuMP.Model,
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variables::AbstractArray{T,N};
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weights::Vector{Float64} = zeros(length(variables)),
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metric::Distances.SemiMetric = Cityblock(),
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) where {T<:Union{VariableRef,AffExpr},N}
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cumulative_distance = objective_function(model)
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vars_vec = [v for v in variables]
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solution = value.(vars_vec)
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# Reset objective sense to be able to update objective function.
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set_objective_sense(model, FEASIBILITY_SENSE)
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# Update objective by adding the distance between variables and the previous optimal solution.
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@objective(
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model,
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Max,
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cumulative_distance + Distances.evaluate(metric, vars_vec, solution)
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)
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end

‎src/NearOptimalAlternatives.jl‎

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using Metaheuristics
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using DataStructures
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include("MGA-Methods/Max-Distance.jl")
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include("results.jl")
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include("alternative-optimisation.jl")
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include("generate-alternatives.jl")

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