diff --git a/lib/OptimizationMetaheuristics/test/runtests.jl b/lib/OptimizationMetaheuristics/test/runtests.jl index 448c3d88a..404afdf33 100644 --- a/lib/OptimizationMetaheuristics/test/runtests.jl +++ b/lib/OptimizationMetaheuristics/test/runtests.jl @@ -115,17 +115,17 @@ Random.seed!(42) "Metaheuristics.Algorithm{CCMO{NSGA2}} for sphere" => [ 1.6659983952552437, 4.731690734657798], "Metaheuristics.Algorithm{MOEAD_DE} for sphere" => [ - 1.3118335977331483, 5.478715622895562], + 0.989671094714782, 6.418963025927054], "Metaheuristics.Algorithm{SMS_EMOA} for sphere" => [ 0.5003293369817386, 7.837151299208113], "Metaheuristics.Algorithm{NSGA2} for rastrigin" => [0.0, 12.0], "Metaheuristics.Algorithm{NSGA3} for rastrigin" => [ - 9.754810555001253, 11.123569741993528], + 7.597191334401674, 8.53603819834027], "Metaheuristics.Algorithm{SPEA2} for rastrigin" => [0.0, 12.0], "Metaheuristics.Algorithm{CCMO{NSGA2}} for rastrigin" => [ 2.600961284360525, 3.4282466721631755], "Metaheuristics.Algorithm{MOEAD_DE} for rastrigin" => [ - 2.4963842982482607, 10.377445766099369], + 2.8812870528400936, 7.145617997943864], "Metaheuristics.Algorithm{SMS_EMOA} for rastrigin" => [0.0, 12.0], "Metaheuristics.Algorithm{NSGA2} for rosenbrock" => [ 17.500214034475118, 586.5039366722865], @@ -136,19 +136,19 @@ Random.seed!(42) "Metaheuristics.Algorithm{CCMO{NSGA2}} for rosenbrock" => [ 2.600961284360525, 3.4282466721631755], "Metaheuristics.Algorithm{MOEAD_DE} for rosenbrock" => [ - 12.969698120217537, 642.4135236259822], + 8.658481667869118, 644.4544222985385], "Metaheuristics.Algorithm{SMS_EMOA} for rosenbrock" => [ 61.6898556398449, 450.62433057243777], "Metaheuristics.Algorithm{NSGA2} for ackley" => [ 2.240787163704834, 5.990002878952371], "Metaheuristics.Algorithm{NSGA3} for ackley" => [ - 3.408535107623966, 5.459538604033934], + 2.186720100012558, 6.125797156949968], "Metaheuristics.Algorithm{SPEA2} for ackley" => [ 4.440892098500626e-16, 6.593599079287213], "Metaheuristics.Algorithm{CCMO{NSGA2}} for ackley" => [ 2.600961284360525, 3.4282466721631755], "Metaheuristics.Algorithm{MOEAD_DE} for ackley" => [ - 4.440892098500626e-16, 6.593599079287213], + 2.982885504039104, 5.052934325547806], "Metaheuristics.Algorithm{SMS_EMOA} for ackley" => [ 3.370770500897429, 5.510527199861947], "Metaheuristics.Algorithm{NSGA2} for dtlz2" => [ @@ -172,7 +172,7 @@ Random.seed!(42) "Metaheuristics.Algorithm{CCMO{NSGA2}} for schaffer_n2" => [ 3.632401400816196e-17, 4.9294679997494206e-17], "Metaheuristics.Algorithm{MOEAD_DE} for schaffer_n2" => [ - 2.50317097527324, 0.17460592430221922], + 1.5886671796558842, 0.5469735282631156], "Metaheuristics.Algorithm{SMS_EMOA} for schaffer_n2" => [ 0.4978888767998813, 1.67543922644328] ) @@ -201,7 +201,7 @@ Random.seed!(42) options = Options(debug = false, iterations = 250)), SMS_EMOA() ] - + Random.seed!(42) # Run tests for each problem and algorithm for (prob_func, lb, ub) in problems prob_name = string(prob_func) @@ -213,7 +213,7 @@ Random.seed!(42) if (alg_name == "Metaheuristics.Algorithm{CCMO{NSGA2}}") sol = solve(prob, alg) else - sol = solve(prob, alg; maxiters = 100, use_initial = true) + sol = solve(prob, alg; maxiters = 10000, use_initial = true) end # Tests