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A set of common benchmark functions for testing optimization algorithms in Julia
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rbalexander1/BenchmarkFunctions.jl
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A set of common benchmark functions for testing optimization algorithms in Julia
To add the package, enter the following into the REPL:
julia>using Pkgjulia> Pkg.add("BenchmarkFunctions")
A simple example for creating a grid and evaluating a benchmark function is:
using BenchmarkFunctionsX=ndgrid(-4:0.1:4,-4:0.1:4)y=himmelblau(X)
To recreate the above plot:
using BenchmarkFunctionsplot("himmelblau",-4:0.1:4,-4:0.1:4)
This package also include some complicated benchmark functions.
The Competition on Real-World Multiobjective Constrained Optimization 2021(RW-MOP-2021) presented 50 benchmark functionsfrom real-world problems for testing multi-objective optimization algorithms.Those problems are available here.
julia> using BenchmarkFunctionsjulia> f, conf = get_RW_MOP_problem(2); # problem 2julia> confDict{Symbol, Any} with 8 entries: :xmin => [0.05, 0.2, 0.2, 0.35, 3.0] :xmax => [0.5, 0.5, 0.6, 0.5, 6.0] :n => 5 :function => "vibrating_platform" :gn => 5 :hn => 0 :fn => 2 :nadir => [-0.00127461, 318.255]UseBenchmarkFunctions.NAME_OF_PROBLEMS_RW_MOP_2021 for full list of problems.
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A set of common benchmark functions for testing optimization algorithms in Julia
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