Update tests for MOO runtests.jl

Added tests for MOO algorithms.

Author: ParasPuneetSingh

lib/OptimizationMetaheuristics/test/runtests.jl

@@ -50,4 +50,144 @@ using Test
 
     sol = solve(prob, WOA(), use_initial = true)
     @test 10 * sol.objective < l1
+
+    # Define the benchmark functions as multi-objective problems
+function sphere(x)
+    f1 = sum(x .^ 2)
+    f2 = sum((x .- 2.0) .^ 2)
+    gx = [0.0]
+    hx = [0.0]
+    return [f1, f2], gx, hx
+end
+
+function rastrigin(x)
+    f1 = sum(x .^ 2 .- 10 .* cos.(2 .* π .* x) .+ 10)
+    f2 = sum((x .- 2.0) .^ 2 .- 10 .* cos.(2 .* π .* (x .- 2.0)) .+ 10)
+    gx = [0.0]
+    hx = [0.0]
+    return [f1, f2], gx, hx
+end
+
+function rosenbrock(x)
+    f1 = sum(100 .* (x[2:end] .- x[1:end-1] .^ 2) .^ 2 .+ (x[1:end-1] .- 1) .^ 2)
+    f2 = sum(100 .* ((x[2:end] .- 2.0) .- (x[1:end-1] .^ 2)) .^ 2 .+ ((x[1:end-1] .- 1.0) .^ 2))
+    gx = [0.0]
+    hx = [0.0]
+    return [f1, f2], gx, hx
+end
+
+function ackley(x)
+    f1 = -20 * exp(-0.2 * sqrt(sum(x .^ 2) / length(x))) - exp(sum(cos.(2 * π .* x)) / length(x)) + 20 + ℯ
+    f2 = -20 * exp(-0.2 * sqrt(sum((x .- 2.0) .^ 2) / length(x))) - exp(sum(cos.(2 * π .* (x .- 2.0))) / length(x)) + 20 + ℯ
+    gx = [0.0]
+    hx = [0.0]
+    return [f1, f2], gx, hx
+end
+
+
+function dtlz2(x)
+    g = sum((x[3:end] .- 0.5) .^ 2)
+    f1 = (1 + g) * cos(x[1] * π / 2) * cos(x[2] * π / 2)
+    f2 = (1 + g) * cos(x[1] * π / 2) * sin(x[2] * π / 2)
+    gx = [0.0]
+    hx = [0.0]
+    return [f1, f2], gx, hx
+end
+
+function schaffer_n2(x)
+    f1 = x[1]^2
+    f2 = (x[1] - 2.0)^2
+    gx = [0.0]
+    hx = [0.0]
+    return [f1, f2], gx, hx
+end
+OBJECTIVES = Dict(
+            "Metaheuristics.Algorithm{NSGA2} for sphere"=> [0.4761274648673104, 7.888859360956367],
+            "Metaheuristics.Algorithm{NSGA3} for sphere"=> [1.1245011962315388, 5.9084439601220105],
+            "Metaheuristics.Algorithm{SPEA2} for sphere"=> [0.45500157273715425, 8.060476156495577],
+            "Metaheuristics.Algorithm{CCMO{NSGA2}} for sphere"=> [0.8537159192703154, 6.721186217733861],
+            "Metaheuristics.Algorithm{MOEAD_DE} for sphere"=> [1.7135443166012259, 4.818225194026158],
+            "Metaheuristics.Algorithm{SMS_EMOA} for sphere"=> [1.1376191314229631, 5.935092118744685],
+            "Metaheuristics.Algorithm{NSGA2} for rastrigin"=> [3.914962881168682, 11.552205533592897],
+            "Metaheuristics.Algorithm{NSGA3} for rastrigin"=> [4.842031386209626, 5.542348181529025],
+            "Metaheuristics.Algorithm{SPEA2} for rastrigin"=> [2.9692594618763835, 10.596356482458171],
+            "Metaheuristics.Algorithm{CCMO{NSGA2}} for rastrigin"=> [0.4152393951206974, 7.953188854042798],
+            "Metaheuristics.Algorithm{MOEAD_DE} for rastrigin"=> [0.0, 12.0],
+            "Metaheuristics.Algorithm{SMS_EMOA} for rastrigin"=> [10.668382998122013, 11.672554721420616],
+            "Metaheuristics.Algorithm{NSGA2} for rosenbrock"=> [13.564144823755003, 608.7768632268896],
+            "Metaheuristics.Algorithm{NSGA3} for rosenbrock"=> [41.32512246661068, 479.9472092328193],
+            "Metaheuristics.Algorithm{SPEA2} for rosenbrock"=> [20.921291737001457, 566.887198567844],
+            "Metaheuristics.Algorithm{CCMO{NSGA2}} for rosenbrock"=> [0.4152393951206974, 7.953188854042798],
+            "Metaheuristics.Algorithm{MOEAD_DE} for rosenbrock"=> [2.215363988408552, 723.1454508385998],
+            "Metaheuristics.Algorithm{SMS_EMOA} for rosenbrock"=> [20.27041333432111, 575.7366151959259],
+            "Metaheuristics.Algorithm{NSGA2} for ackley"=> [3.4438643047130992, 5.9371415671384895],
+            "Metaheuristics.Algorithm{NSGA3} for ackley"=> [3.4659156540969573, 5.287995047899489],
+            "Metaheuristics.Algorithm{SPEA2} for ackley"=> [2.3209460118197716, 5.918573168574383],
+            "Metaheuristics.Algorithm{CCMO{NSGA2}} for ackley"=> [0.4152393951206974, 7.953188854042798],
+            "Metaheuristics.Algorithm{MOEAD_DE} for ackley"=> [4.440892098500626e-16, 6.593599079287213],
+            "Metaheuristics.Algorithm{SMS_EMOA} for ackley"=> [2.4079028491253074, 6.085847745455787],
+            "Metaheuristics.Algorithm{NSGA2} for dtlz2"=> [0.0008621981163705847, 0.016776532222616037],
+            "Metaheuristics.Algorithm{NSGA3} for dtlz2"=> [0.00530717096691627, 0.006810762449448562],
+            "Metaheuristics.Algorithm{SPEA2} for dtlz2"=> [0.0022573638805422967, 0.0012875185095928014],
+            "Metaheuristics.Algorithm{CCMO{NSGA2}} for dtlz2"=> [2.9276186095638996, 3.0744092709040185],
+            "Metaheuristics.Algorithm{MOEAD_DE} for dtlz2"=> [0.0009460864848779976, 0.015153151632789923],
+            "Metaheuristics.Algorithm{SMS_EMOA} for dtlz2"=> [0.006063356611750317, 0.014614126585905095],
+            "Metaheuristics.Algorithm{NSGA2} for schaffer_n2"=> [1.0978202866371685, 0.9067435054036517],
+            "Metaheuristics.Algorithm{NSGA3} for schaffer_n2"=> [2.755035084049435, 0.11571574056316469],
+            "Metaheuristics.Algorithm{SPEA2} for schaffer_n2"=> [2.2990190172651723, 0.23401248171694122],
+            "Metaheuristics.Algorithm{CCMO{NSGA2}} for schaffer_n2"=> [0.0, 800.0],
+            "Metaheuristics.Algorithm{MOEAD_DE} for schaffer_n2"=> [0.0017365039124724727, 3.8350509838468123],
+            "Metaheuristics.Algorithm{SMS_EMOA} for schaffer_n2"=> [0.7559493982502018, 1.278135376195079],
+    )
+    # Define the testset
+@testset "Multi-Objective Optimization with Various Functions and Metaheuristics" begin
+    # Define the problems and their bounds
+    problems = [
+        (sphere, [0.0, 0.0, 0.0], [1.0, 1.0, 1.0]),
+        (rastrigin, [0.0, 0.0, 0.0], [1.0, 1.0, 1.0]),
+        (rosenbrock, [0.0, 0.0, 0.0], [1.0, 1.0, 1.0]),
+        (ackley, [0.0, 0.0, 0.0], [1.0, 1.0, 1.0]),
+        (dtlz2, [0.0, 0.0, 0.0], [1.0, 1.0, 1.0]),
+        (schaffer_n2, [0.0, 0.0, 0.0], [2.0, 0.0, 0.0])
+    ]
+
+    nobjectives = 2
+    npartitions = 100
+
+    # Define the different algorithms
+    algs = [
+        NSGA2(),
+        NSGA3(),
+        SPEA2(),
+        CCMO(NSGA2(N=100, p_m=0.001)),
+        MOEAD_DE(gen_ref_dirs(nobjectives, npartitions), options=Options(debug=false, iterations = 250)),
+        SMS_EMOA()
+    ]
+
+    # Run tests for each problem and algorithm
+    for (prob_func, lb, ub) in problems
+        prob_name = string(prob_func)
+        for alg in algs
+            alg_name = string(typeof(alg))
+            @testset "$alg_name on $prob_name" begin
+                multi_obj_fun = MultiObjectiveOptimizationFunction((x, p) -> prob_func(x))
+                prob = OptimizationProblem(multi_obj_fun, lb; lb = lb, ub = ub)
+                if (alg_name=="Metaheuristics.Algorithm{CCMO{NSGA2}}")
+                    sol = solve(prob, alg)
+                else
+                    sol = solve(prob, alg; maxiters = 100, use_initial = true)
+                end
+
+                # Tests
+                @test !isempty(sol.minimizer)  # Check that a solution was found
+                
+                # Use sol.objective to get the objective values
+                key = "$alg_name for $prob_name"
+                value = OBJECTIVES[key]
+                objectives = sol.objective
+                @test value==objectives
+                end
+            end
+        end
+    end
 end