format

Author: Vaibhavdixit02

docs/src/index.md

@@ -170,9 +170,7 @@ to add the specific wrapper packages.
     - Unconstrained: ✅
 </details>
 🟡 = supported in downstream library but not yet implemented in `Optimization.jl`; PR to add this functionality are welcome
-
 ## Citation
-
 ```
 @software{vaibhav_kumar_dixit_2023_7738525,
 	author = {Vaibhav Kumar Dixit and Christopher Rackauckas},

docs/src/tutorials/remakecomposition.md

@@ -26,14 +26,15 @@ end
 
 optf = OptimizationFunction(f_schwefel, Optimization.AutoReverseDiff(compile = true))
 
-x0 = ones(10).*200.0
-prob = OptimizationProblem(optf, x0, [418.9829], lb = fill(-500.0, 10), ub = fill(500.0, 10))
+x0 = ones(10) .* 200.0
+prob = OptimizationProblem(
+    optf, x0, [418.9829], lb = fill(-500.0, 10), ub = fill(500.0, 10))
 
 @show f_schwefel(x0)
 ```
 
-Our polyalgorithm strategy will to use BlackBoxOptim's global optimizers for efficient exploration of the 
-parameter space followed by a quasi-Newton LBFGS method to (hopefully) converge to the global 
+Our polyalgorithm strategy will to use BlackBoxOptim's global optimizers for efficient exploration of the
+parameter space followed by a quasi-Newton LBFGS method to (hopefully) converge to the global
 optimum.
 
 ```@example polyalg

lib/OptimizationBBO/src/OptimizationBBO.jl

@@ -17,7 +17,7 @@ for j in string.(BlackBoxOptim.SingleObjectiveMethodNames)
 end
 
 Base.@kwdef struct BBO_borg_moea <: BBO
-    method = :borg_moea 
+    method = :borg_moea
 end
 export BBO_borg_moea
 

lib/OptimizationBBO/test/runtests.jl

@@ -49,68 +49,72 @@ using Test
     end
 
     # Define the initial guess and bounds
-u0 = [0.25, 0.25]
-lb = [0.0, 0.0]
-ub = [2.0, 2.0]
+    u0 = [0.25, 0.25]
+    lb = [0.0, 0.0]
+    ub = [2.0, 2.0]
 
-# Define the optimizer
-opt = OptimizationBBO.BBO_borg_moea()
+    # Define the optimizer
+    opt = OptimizationBBO.BBO_borg_moea()
 
-@testset "Multi-Objective Optimization Tests" begin
+    @testset "Multi-Objective Optimization Tests" begin
 
-    # Test 1: Sphere and Rastrigin Functions
-    @testset "Sphere and Rastrigin Functions" begin
-        function multi_obj_func_1(x, p)
-            f1 = sum(x .^ 2)  # Sphere function
-            f2 = sum(x .^ 2 .- 10 .* cos.(2π .* x) .+ 10)  # Rastrigin function
-            return (f1, f2)
+        # Test 1: Sphere and Rastrigin Functions
+        @testset "Sphere and Rastrigin Functions" begin
+            function multi_obj_func_1(x, p)
+                f1 = sum(x .^ 2)  # Sphere function
+                f2 = sum(x .^ 2 .- 10 .* cos.(2π .* x) .+ 10)  # Rastrigin function
+                return (f1, f2)
+            end
+
+            mof_1 = MultiObjectiveOptimizationFunction(multi_obj_func_1)
+            prob_1 = Optimization.OptimizationProblem(mof_1, u0; lb = lb, ub = ub)
+            sol_1 = solve(prob_1, opt, NumDimensions = 2,
+                FitnessScheme = ParetoFitnessScheme{2}(is_minimizing = true))
+
+            @test sol_1 ≠ nothing
+            println("Solution for Sphere and Rastrigin: ", sol_1)
+            @test sol_1.objective[1]≈6.9905986e-18 atol=1e-3
+            @test sol_1.objective[2]≈1.7763568e-15 atol=1e-3
         end
-        
-        mof_1 = MultiObjectiveOptimizationFunction(multi_obj_func_1)
-        prob_1 = Optimization.OptimizationProblem(mof_1, u0; lb=lb, ub=ub)
-        sol_1 = solve(prob_1, opt, NumDimensions=2, FitnessScheme=ParetoFitnessScheme{2}(is_minimizing=true))
-        
-        @test sol_1 ≠ nothing
-        println("Solution for Sphere and Rastrigin: ", sol_1)
-        @test sol_1.objective[1] ≈ 6.9905986e-18 atol=1e-3
-        @test sol_1.objective[2] ≈ 1.7763568e-15 atol=1e-3
-    end
 
-    # Test 2: Rosenbrock and Ackley Functions
-    @testset "Rosenbrock and Ackley Functions" begin
-        function multi_obj_func_2(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)
+        # Test 2: Rosenbrock and Ackley Functions
+        @testset "Rosenbrock and Ackley Functions" begin
+            function multi_obj_func_2(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)
+            end
+
+            mof_2 = MultiObjectiveOptimizationFunction(multi_obj_func_2)
+            prob_2 = Optimization.OptimizationProblem(mof_2, u0; lb = lb, ub = ub)
+            sol_2 = solve(prob_2, opt, NumDimensions = 2,
+                FitnessScheme = ParetoFitnessScheme{2}(is_minimizing = true))
+
+            @test sol_2 ≠ nothing
+            println("Solution for Rosenbrock and Ackley: ", sol_2)
+            @test sol_2.objective[1]≈0.97438 atol=1e-3
+            @test sol_2.objective[2]≈0.04088 atol=1e-3
         end
-        
-        mof_2 = MultiObjectiveOptimizationFunction(multi_obj_func_2)
-        prob_2 = Optimization.OptimizationProblem(mof_2, u0; lb=lb, ub=ub)
-        sol_2 = solve(prob_2, opt, NumDimensions=2, FitnessScheme=ParetoFitnessScheme{2}(is_minimizing=true))
-        
-        @test sol_2 ≠ nothing
-        println("Solution for Rosenbrock and Ackley: ", sol_2)
-        @test sol_2.objective[1] ≈ 0.97438 atol=1e-3
-        @test sol_2.objective[2] ≈ 0.04088 atol=1e-3
-    end
 
-    # Test 3: ZDT1 Function
-    @testset "ZDT1 Function" begin
-        function multi_obj_func_3(x, p)
-            f1 = x[1]
-            g = 1 + 9 * sum(x[2:end]) / (length(x) - 1)
-            f2 = g * (1 - sqrt(f1 / g))
-            return (f1, f2)
+        # Test 3: ZDT1 Function
+        @testset "ZDT1 Function" begin
+            function multi_obj_func_3(x, p)
+                f1 = x[1]
+                g = 1 + 9 * sum(x[2:end]) / (length(x) - 1)
+                f2 = g * (1 - sqrt(f1 / g))
+                return (f1, f2)
+            end
+
+            mof_3 = MultiObjectiveOptimizationFunction(multi_obj_func_3)
+            prob_3 = Optimization.OptimizationProblem(mof_3, u0; lb = lb, ub = ub)
+            sol_3 = solve(prob_3, opt, NumDimensions = 2,
+                FitnessScheme = ParetoFitnessScheme{2}(is_minimizing = true))
+
+            @test sol_3 ≠ nothing
+            println("Solution for ZDT1: ", sol_3)
+            @test sol_3.objective[1]≈0.273445 atol=1e-3
+            @test sol_3.objective[2]≈0.477079 atol=1e-3
         end
-        
-        mof_3 = MultiObjectiveOptimizationFunction(multi_obj_func_3)
-        prob_3 = Optimization.OptimizationProblem(mof_3, u0; lb=lb, ub=ub)
-        sol_3 = solve(prob_3, opt, NumDimensions=2, FitnessScheme=ParetoFitnessScheme{2}(is_minimizing=true))
-        
-        @test sol_3 ≠ nothing
-        println("Solution for ZDT1: ", sol_3)
-        @test sol_3.objective[1] ≈ 0.273445 atol=1e-3
-        @test sol_3.objective[2] ≈ 0.477079 atol=1e-3
     end
 end
-end

lib/OptimizationEvolutionary/src/OptimizationEvolutionary.jl

@@ -146,13 +146,15 @@ function SciMLBase.__solve(cache::OptimizationCache{
             cons = WorstFitnessConstraints(Float64[], Float64[], cache.lcons, cache.ucons,
                 c)
             if isa(f, MultiObjectiveOptimizationFunction)
-                opt_res = Evolutionary.optimize(_loss, _loss(cache.u0), cons, cache.u0, cache.opt, opt_args)
+                opt_res = Evolutionary.optimize(
+                    _loss, _loss(cache.u0), cons, cache.u0, cache.opt, opt_args)
             else
                 opt_res = Evolutionary.optimize(_loss, cons, cache.u0, cache.opt, opt_args)
             end
         else
             if isa(f, MultiObjectiveOptimizationFunction)
-                opt_res = Evolutionary.optimize(_loss, _loss(cache.u0), cache.u0, cache.opt, opt_args)
+                opt_res = Evolutionary.optimize(
+                    _loss, _loss(cache.u0), cache.u0, cache.opt, opt_args)
             else
                 opt_res = Evolutionary.optimize(_loss, cache.u0, cache.opt, opt_args)
             end
@@ -165,9 +167,10 @@ function SciMLBase.__solve(cache::OptimizationCache{
             cons = BoxConstraints(cache.lb, cache.ub)
         end
         if isa(f, MultiObjectiveOptimizationFunction)
-                opt_res = Evolutionary.optimize(_loss, _loss(cache.u0), cons, cache.u0, cache.opt, opt_args)
+            opt_res = Evolutionary.optimize(
+                _loss, _loss(cache.u0), cons, cache.u0, cache.opt, opt_args)
         else
-                opt_res = Evolutionary.optimize(_loss, cons, cache.u0, cache.opt, opt_args)
+            opt_res = Evolutionary.optimize(_loss, cons, cache.u0, cache.opt, opt_args)
         end
     end
     t1 = time()
@@ -176,17 +179,17 @@ function SciMLBase.__solve(cache::OptimizationCache{
         time = t1 - t0, fevals = opt_res.f_calls)
     if !isa(f, MultiObjectiveOptimizationFunction)
         SciMLBase.build_solution(cache, cache.opt,
-        Evolutionary.minimizer(opt_res),
-        Evolutionary.minimum(opt_res); original = opt_res,
-        retcode = opt_ret,
-        stats = stats)
+            Evolutionary.minimizer(opt_res),
+            Evolutionary.minimum(opt_res); original = opt_res,
+            retcode = opt_ret,
+            stats = stats)
     else
         ans = Evolutionary.minimizer(opt_res)
         SciMLBase.build_solution(cache, cache.opt,
-        ans,
-        _loss(ans[1]); original = opt_res,
-        retcode = opt_ret,
-        stats = stats)
+            ans,
+            _loss(ans[1]); original = opt_res,
+            retcode = opt_ret,
+            stats = stats)
     end
 end
 

lib/OptimizationEvolutionary/test/runtests.jl

@@ -61,12 +61,12 @@ Random.seed!(1234)
     # 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)
+        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)
+        obj_func = MultiObjectiveOptimizationFunction(func, jac = gradient_multi_objective)
 
         # Set up the evolutionary algorithm (e.g., NSGA2)
         algorithm = OptimizationEvolutionary.NSGA2()
@@ -84,39 +84,40 @@ Random.seed!(1234)
 
         # Test 1: Sphere and Rastrigin Functions
         @testset "Sphere and Rastrigin Functions" begin
-            function multi_objective_1(x, p=nothing)::Vector{Float64}
+            function multi_objective_1(x, p = nothing)::Vector{Float64}
                 f1 = sum(x .^ 2)  # Sphere function
                 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=1e-3
-            @test result.u[1][2] ≈ 4.96471e-5 atol=1e-3
-            @test result.objective[1] ≈ 8.6879e-9 atol=1e-3
-            @test result.objective[2] ≈ 1.48875349381683e-6 atol=1e-3
+            @test result.u[1][1]≈7.88866e-5 atol=1e-3
+            @test result.u[1][2]≈4.96471e-5 atol=1e-3
+            @test result.objective[1]≈8.6879e-9 atol=1e-3
+            @test result.objective[2]≈1.48875349381683e-6 atol=1e-3
         end
 
         # Test 2: Rosenbrock and Ackley Functions
         @testset "Rosenbrock and Ackley Functions" begin
-            function multi_objective_2(x, p=nothing)::Vector{Float64}
+            function multi_objective_2(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
+                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]
             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=1e-3
-            @test result.u[1][2] ≈ 0.001433311246712721 atol=1e-3
-            @test result.objective[1] ≈ 0.9922302888530358 atol=1e-3
-            @test result.objective[2] ≈ 0.012479470703588902 atol=1e-3
+            @test result.u[1][1]≈0.003993274873103834 atol=1e-3
+            @test result.u[1][2]≈0.001433311246712721 atol=1e-3
+            @test result.objective[1]≈0.9922302888530358 atol=1e-3
+            @test result.objective[2]≈0.012479470703588902 atol=1e-3
         end
 
         # Test 3: ZDT1 Function
         @testset "ZDT1 Function" begin
-            function multi_objective_3(x, p=nothing)::Vector{Float64}
+            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
@@ -126,42 +127,42 @@ Random.seed!(1234)
             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=1e-3
-            @test result.u[1][2] ≈ 1.22128 atol=1e-3
-            @test result.objective[1] ≈ -0.365434 atol=1e-3
+            @test result.u[1][1]≈-0.365434 atol=1e-3
+            @test result.u[1][2]≈1.22128 atol=1e-3
+            @test result.objective[1]≈-0.365434 atol=1e-3
             @test isnan(result.objective[2])
         end
 
         # Test 4: DTLZ2 Function
         @testset "DTLZ2 Function" begin
-            function multi_objective_4(x, p=nothing)::Vector{Float64}
+            function multi_objective_4(x, p = nothing)::Vector{Float64}
                 f1 = (1 + sum(x[2:end] .^ 2)) * cos(x[1] * π / 2)
                 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=1e-3
-            @test result.u[2][1] ≈ 0.713992 atol=1e-3
-            @test result.objective[1] ≈ 0.1599915 atol=1e-3
-            @test result.objective[2] ≈ 1.001824893932647 atol=1e-3
+            @test result.u[1][1]≈0.899183 atol=1e-3
+            @test result.u[2][1]≈0.713992 atol=1e-3
+            @test result.objective[1]≈0.1599915 atol=1e-3
+            @test result.objective[2]≈1.001824893932647 atol=1e-3
         end
 
         # Test 5: Schaffer Function N.2
         @testset "Schaffer Function N.2" begin
-            function multi_objective_5(x, p=nothing)::Vector{Float64}
+            function multi_objective_5(x, p = nothing)::Vector{Float64}
                 f1 = x[1]^2
                 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=1e-3
-            @test result.u[9][1] ≈ 1.0 atol=1e-3
-            @test result.objective[1] ≈ 1.0 atol=1e-3
-            @test result.objective[2] ≈ 1.0 atol=1e-3
+            @test result.u[19][1]≈0.252635 atol=1e-3
+            @test result.u[9][1]≈1.0 atol=1e-3
+            @test result.objective[1]≈1.0 atol=1e-3
+            @test result.objective[2]≈1.0 atol=1e-3
         end
     end
 end