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Fix OptimizationEvolutionary multi-objective core tests #1250

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Fix OptimizationEvolutionary multi-objective core tests #1250

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7f4a628
Fix OptimizationEvolutionary multi-objective core tests
ChrisRackauckas Jul 8, 2026
f979ebe
Add Evolutionary downgrade compat bound
ChrisRackauckas Jul 8, 2026
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4 changes: 4 additions & 0 deletions lib/OptimizationEvolutionary/Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,9 @@ Evolutionary = "86b6b26d-c046-49b6-aa0b-5f0f74682bd6"
SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
Reexport = "189a3867-3050-52da-a836-e630ba90ab69"

[weakdeps]
LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"

[extras]
Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
Expand All @@ -19,6 +22,7 @@ OptimizationBase = {path = "../OptimizationBase"}

[compat]
Evolutionary = "0.11"
LogExpFunctions = "0.3.28"
OptimizationBase = "5"
Pkg = "1"
Random = "1.10"
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4 changes: 1 addition & 3 deletions lib/OptimizationEvolutionary/src/OptimizationEvolutionary.jl
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Expand Up @@ -157,9 +157,7 @@ function SciMLBase.__solve(cache::OptimizationCache{O}) where {
# Passing the initial point causes the population to be copies of that point,
# which prevents proper exploration of the search space.
if isa(f, MultiObjectiveOptimizationFunction)
opt_res = Evolutionary.optimize(
_loss, _loss(cache.u0), cons, cache.opt, opt_args
)
opt_res = Evolutionary.optimize(_loss, cons, cache.opt, opt_args)
else
opt_res = Evolutionary.optimize(_loss, cons, cache.opt, opt_args)
end
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122 changes: 67 additions & 55 deletions lib/OptimizationEvolutionary/test/core_tests.jl
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Expand Up @@ -68,26 +68,38 @@ Random.seed!(1234)
@test haskey(sol.original.trace[end].metadata, "TESTVAL") &&
haskey(sol.original.trace[end].metadata, "curr_u")

# Test Suite for Different Multi-Objective Functions
function test_multi_objective(func, initial_guess)
# Define the gradient function using ForwardDiff
function gradient_multi_objective(x, p = nothing)
ForwardDiff.jacobian(func, x)
end

# Create an instance of MultiObjectiveOptimizationFunction
obj_func = MultiObjectiveOptimizationFunction(func, jac = gradient_multi_objective)

# Set up the evolutionary algorithm (e.g., NSGA2)
# NSGA2 returns stochastic Pareto candidates; assert wrapper invariants instead of
# exact population indices, which can change across Evolutionary releases.
function test_multi_objective(func, initial_guess; seed, lb = nothing, ub = nothing)
Random.seed!(seed)
obj_func = MultiObjectiveOptimizationFunction(func)
algorithm = OptimizationEvolutionary.NSGA2()
problem = if lb === nothing && ub === nothing
OptimizationProblem(obj_func, initial_guess)
else
OptimizationProblem(obj_func, initial_guess; lb = lb, ub = ub)
end
return solve(problem, algorithm)
end

# Define the optimization problem
problem = OptimizationProblem(obj_func, initial_guess)

# Solve the optimization problem
result = solve(problem, algorithm)

return result
function check_multi_objective_result(
result, func, initial_guess; lb = nothing, ub = nothing, min_population = 1
)
@test result !== nothing
@test result.u isa AbstractVector
@test !isempty(result.u)
@test length(result.u) >= min_population
@test length(unique(result.u)) >= min_population
@test all(u -> u isa AbstractVector && length(u) == length(initial_guess), result.u)

objective_values = [func(u, nothing) for u in result.u]
@test result.objective == objective_values[1]
@test all(obj -> length(obj) == length(result.objective), objective_values)
@test all(obj -> all(isfinite, obj), objective_values)

if lb !== nothing && ub !== nothing
@test all(u -> all((lb .<= u) .& (u .<= ub)), result.u)
end
end

@testset "Multi-Objective Optimization Tests" begin
Expand All @@ -99,13 +111,8 @@ Random.seed!(1234)
f2 = sum(x .^ 2 .- 10 .* cos.(2π .* x) .+ 10) # Rastrigin function
return [f1, f2]
end
result = test_multi_objective(multi_objective_1, [0.0, 1.0])
@test result ≠ nothing
println("Solution for Sphere and Rastrigin: ", result)
@test result.u[1][1] ≈ 7.88866e-5 atol = 1.0e-3
@test result.u[1][2] ≈ 4.96471e-5 atol = 1.0e-3
@test result.objective[1] ≈ 8.6879e-9 atol = 1.0e-3
@test result.objective[2] ≈ 1.48875349381683e-6 atol = 1.0e-3
result = test_multi_objective(multi_objective_1, [0.0, 1.0]; seed = 1101)
check_multi_objective_result(result, multi_objective_1, [0.0, 1.0])
end

# Test 2: Rosenbrock and Ackley Functions
Expand All @@ -116,31 +123,30 @@ Random.seed!(1234)
exp(0.5 * (cos(2π * x[1]) + cos(2π * x[2]))) + exp(1) + 20.0 # Ackley function
return [f1, f2]
end
result = test_multi_objective(multi_objective_2, [0.1, 1.0])
@test result ≠ nothing
println("Solution for Rosenbrock and Ackley: ", result)
@test result.u[1][1] ≈ 0.003993274873103834 atol = 1.0e-3
@test result.u[1][2] ≈ 0.001433311246712721 atol = 1.0e-3
@test result.objective[1] ≈ 0.9922302888530358 atol = 1.0e-3
@test result.objective[2] ≈ 0.012479470703588902 atol = 1.0e-3
result = test_multi_objective(multi_objective_2, [0.1, 1.0]; seed = 1102)
check_multi_objective_result(
result, multi_objective_2, [0.1, 1.0]; min_population = 2
)
end

# Test 3: ZDT1 Function
@testset "ZDT1 Function" begin
function multi_objective_3(x, p = nothing)::Vector{Float64}
f1 = x[1]
g = 1 + 9 * sum(x[2:end]) / (length(x) - 1)
sqrt_arg = f1 / g
f2 = g * (1 - (sqrt_arg >= 0 ? sqrt(sqrt_arg) : NaN))
f2 = g * (1 - sqrt(f1 / g))
return [f1, f2]
end
result = test_multi_objective(multi_objective_3, [0.25, 1.5])
@test result ≠ nothing
println("Solution for ZDT1: ", result)
@test result.u[1][1] ≈ -0.365434 atol = 1.0e-3
@test result.u[1][2] ≈ 1.22128 atol = 1.0e-3
@test result.objective[1] ≈ -0.365434 atol = 1.0e-3
@test isnan(result.objective[2])
lb = zeros(2)
ub = ones(2)
initial_guess = [0.25, 0.75]
result = test_multi_objective(
multi_objective_3, initial_guess; seed = 1103, lb = lb, ub = ub
)
check_multi_objective_result(
result, multi_objective_3, initial_guess; lb = lb, ub = ub,
min_population = 2
)
end

# Test 4: DTLZ2 Function
Expand All @@ -150,13 +156,16 @@ Random.seed!(1234)
f2 = (1 + sum(x[2:end] .^ 2)) * sin(x[1] * π / 2)
return [f1, f2]
end
result = test_multi_objective(multi_objective_4, [0.25, 0.75])
@test result ≠ nothing
println("Solution for DTLZ2: ", result)
@test result.u[1][1] ≈ 0.899183 atol = 1.0e-3
@test result.u[2][1] ≈ 0.713992 atol = 1.0e-3
@test result.objective[1] ≈ 0.1599915 atol = 1.0e-3
@test result.objective[2] ≈ 1.001824893932647 atol = 1.0e-3
lb = zeros(2)
ub = ones(2)
initial_guess = [0.25, 0.75]
result = test_multi_objective(
multi_objective_4, initial_guess; seed = 1104, lb = lb, ub = ub
)
check_multi_objective_result(
result, multi_objective_4, initial_guess; lb = lb, ub = ub,
min_population = 2
)
end

# Test 5: Schaffer Function N.2
Expand All @@ -166,13 +175,16 @@ Random.seed!(1234)
f2 = (x[1] - 2)^2
return [f1, f2]
end
result = test_multi_objective(multi_objective_5, [1.0])
@test result ≠ nothing
println("Solution for Schaffer N.2: ", result)
@test result.u[19][1] ≈ 0.252635 atol = 1.0e-3
@test result.u[9][1] ≈ 1.0 atol = 1.0e-3
@test result.objective[1] ≈ 1.0 atol = 1.0e-3
@test result.objective[2] ≈ 1.0 atol = 1.0e-3
lb = [0.0]
ub = [2.0]
initial_guess = [1.0]
result = test_multi_objective(
multi_objective_5, initial_guess; seed = 1105, lb = lb, ub = ub
)
check_multi_objective_result(
result, multi_objective_5, initial_guess; lb = lb, ub = ub,
min_population = 2
)
end
end
end
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