Docs for MOO #823
Docs for MOO #823
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Added documentation for MOO in BBO
MOO docs update.
MOO docs update.
Vaibhavdixit02
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https://github.com/SciML/Optimization.jl/actions/runs/10977080544/job/30478629355?pr=823 documentation failure seems real, can you take a look? |
updated project.toml for the docs.
Added compat for BBO.
| end | ||
| mof = MultiObjectiveOptimizationFunction(multi_obj_func_2) | ||
| prob = Optimization.OptimizationProblem(mof, u0; lb = [0.0, 0.0], ub = [2.0, 2.0]) | ||
| sol = solve(prob_2, opt, NumDimensions=2, FitnessScheme=ParetoFitnessScheme{2}(is_minimizing=true)) |
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| sol = solve(prob_2, opt, NumDimensions=2, FitnessScheme=ParetoFitnessScheme{2}(is_minimizing=true)) | |
| sol = solve(prob, opt, NumDimensions=2, FitnessScheme=ParetoFitnessScheme{2}(is_minimizing=true)) |
| return [f1, f2] | ||
| end | ||
| initial_guess = [1.0, 1.0] | ||
| function gradient_multi_objective(x, p=nothing) |
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Since this isn't used please remove it
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It is used in the MultiObjectiveOptimizationFunction call below and is passed as the jac arg.
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But Evolutionary doesn't need derivatives right?
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Yeah just checked the testing once again and it is not needed, so will remove it.
| npartitions = 100 | ||
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| # reference points (Das and Dennis's method) | ||
| weights = gen_ref_dirs(nobjectives, npartitions) |
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This function is missing here
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@ParasPuneetSingh please take a look at the review above |
Added required packages for MOO docs.
added required packages for MOO
Corrected function names for MOO docs.
Removed unnecessary FowardDiff function.
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Can you rebase on master there was an incorrect compat there that made the docs environment fail here I have updated it |
Added the package for the algorithms.
| @@ -67,3 +67,21 @@ prob = Optimization.OptimizationProblem(f, x0, p, lb = [-1.0, -1.0], ub = [1.0, | |||
| sol = solve(prob, BBO_adaptive_de_rand_1_bin_radiuslimited(), maxiters = 100000, | |||
| maxtime = 1000.0) | |||
| ``` | |||
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| ## Multi-objective optimization | |||
| The optimizer for Multi-Objective Optimization is `BBO_borg_moea()`. Your objective function should return a tuple of the objective values and you should indicate the fitness scheme to be (typically) Pareto fitness and specify the number of objectives. Otherwise, the use is similar, here is an example: | |||
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Why a tuple? That is not going to scale well.
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Yeah corrected that mistake to vector, the struct uses a vector of objective functions.
| end | ||
| mof = MultiObjectiveOptimizationFunction(multi_obj_func) | ||
| prob = Optimization.OptimizationProblem(mof, u0; lb = [0.0, 0.0], ub = [2.0, 2.0]) | ||
| sol = solve(prob, opt, NumDimensions=2, FitnessScheme=ParetoFitnessScheme{2}(is_minimizing=true)) |
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NumDimensions, FitnessScheme, those don't match Julia style.
| function func(x, p=nothing)::Vector{Float64} | ||
| f1 = (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2 # Rosenbrock function | ||
| f2 = -20.0 * exp(-0.2 * sqrt(0.5 * (x[1]^2 + x[2]^2))) - exp(0.5 * (cos(2π * x[1]) + cos(2π * x[2]))) + exp(1) + 20.0 # Ackley function | ||
| return [f1, f2] |
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it's an array now? Is there an in-place form?
| # In this example, we have no constraints | ||
| gx = [0.0] # Inequality constraints (not used) | ||
| hx = [0.0] # Equality constraints (not used) | ||
| return [f1, f2], gx, hx |
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This is now a third API?
Added evolutionary to the package.
updated algorithm call.
Correction of changeing tuple to vector.
| function multi_obj_func(x, p) | ||
| f1 = (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2 # Rosenbrock function | ||
| f2 = -20.0 * exp(-0.2 * sqrt(0.5 * (x[1]^2 + x[2]^2))) - exp(0.5 * (cos(2π * x[1]) + cos(2π * x[2]))) + exp(1) + 20.0 # Ackley function | ||
| return (f1, f2) |
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you didn't actually change it though?
corrected algorithm calls.
Checklist
contributor guidelines, in particular the SciML Style Guide and
COLPRAC.
Additional context
Add any other context about the problem here.