add ParallelPopulationOptimizer

src/BlackBoxOptim.jl

@@ -6,6 +6,7 @@ using Distributions, StatsBase, Compat
 
 export  Optimizer, AskTellOptimizer, SteppingOptimizer, PopulationOptimizer,
         bboptimize, bbsetup, compare_optimizers,
+        ParallelPopulationOptimizer,
 
         DiffEvoOpt, de_rand_1_bin, de_rand_1_bin_radiuslimited,
         adaptive_de_rand_1_bin, adaptive_de_rand_1_bin_radiuslimited,
@@ -179,6 +180,7 @@ include("resampling_memetic_search.jl")
 include("simultaneous_perturbation_stochastic_approximation.jl")
 include("generating_set_search.jl")
 include("direct_search_with_probabilistic_descent.jl")
+include("parallel_population_optimizer.jl")
 
 # Fitness
 # include("fitness/fitness_types.jl") FIXME merge it with fitness.jl

src/opt_controller.jl

@@ -316,3 +316,20 @@ make_opt_results{O<:PopulationOptimizer}(ctrl::OptRunController{O}, oc::OptContr
     num_func_evals(ctrl),
     population(ctrl.optimizer)
   )
+
+# HACK return population results, although ParallelPopulationOptimizer is not
+# populaton optimizer
+make_opt_results{O<:ParallelPopulationOptimizer}(ctrl::OptRunController{O}, oc::OptController{O}) =
+  PopulationOptimizationResults{fitness_type(problem(ctrl)), Individual,
+                                typeof(population(ctrl.optimizer))}(
+    string(oc.parameters[:Method]),
+    best_fitness(ctrl),
+    best_candidate(ctrl),
+    stop_reason(ctrl),
+    num_steps(ctrl),
+    start_time(ctrl),
+    elapsed_time(ctrl),
+    oc.parameters,
+    num_func_evals(ctrl),
+    population(ctrl.optimizer)
+  )

src/optimization_methods.jl

@@ -14,6 +14,7 @@ const ValidMethods = @compat Dict{Symbol,Union(Any,Function)}(
   :simultaneous_perturbation_stochastic_approximation => SimultaneousPerturbationSA2,
   :generating_set_search => GeneratingSetSearcher,
   :probabilistic_descent => direct_search_probabilistic_descent,
+  :parallel_population_optimizer => parallel_population_optimizer,
 )
 
 const MethodNames = sort!(collect(keys(ValidMethods)))

test/helper.jl

@@ -3,3 +3,8 @@ using FactCheck
 using Compat
 
 NumTestRepetitions = 100
+
+if nprocs() < 4
+  addprocs(4-nprocs()) # add procs for parallel population optimizer
+end
+@everywhere using BlackBoxOptim

test/runtests.jl

@@ -18,6 +18,7 @@ my_tests = [
   "test_differential_evolution.jl",
   "test_adaptive_differential_evolution.jl",
   "test_natural_evolution_strategies.jl",
+  "test_parallel_population_optimizer.jl",
 
   "test_toplevel_bboptimize.jl",
   "test_smoketest_bboptimize.jl",

test/test_parallel_population_optimizer.jl

@@ -0,0 +1,45 @@
+facts("Parallel population optimizer") do
+
+@everywhere using BlackBoxOptim
+
+context("optimizing small problem") do
+  rosenbrock2d(x) = (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2
+
+  res = bboptimize(rosenbrock2d; Method = :parallel_population_optimizer,
+      SearchSpace = [(-5.0, 5.0), (-2.0, 2.0)], MaxTime = 100.0,
+      ShowTrace = true, MigrationSize = 2, MigrationPeriod = 100)
+  @fact size(best_candidate(res)) => (2,)
+  @fact typeof(best_fitness(res)) => Float64
+  @fact best_fitness(res) => less_than(100.0)
+  pop = population(res)
+  @fact numdims(pop) --> 2
+  @fact popsize(pop) > 0 --> true
+end
+
+context("propagating exceptions from workers to the master") do
+  # 0.01 chance to get domain error
+  bogus(x) = sqrt(x[1]-0.01)
+  # 0.01 chance to raise exception prematurely during bbsetup()
+  res = bbsetup(bogus; Method = :parallel_population_optimizer,
+      SearchSpace = [(0.0, 1.0)], MaxTime = 100.0,
+        ShowTrace = true, MigrationSize = 2, MigrationPeriod = 100)
+  @fact_throws RemoteException bboptimize(res)
+end
+
+#= doesn't work with PopulationMatrix
+context("worker method that uses PopulationMatrix") do
+  rosenbrock2d(x) = (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2
+
+  res = bboptimize(rosenbrock2d; Method = :parallel_population_optimizer,
+      WorkerMethod = :separable_nes,
+      SearchSpace = [(-5.0, 5.0), (-2.0, 2.0)], MaxTime = 100.0,
+      ShowTrace = true, MigrationSize = 2, MigrationPeriod = 100)
+  @fact size(best_candidate(res)) => (2,)
+  @fact typeof(best_fitness(res)) => Float64
+  @fact best_fitness(res) => less_than(100.0)
+  pop = population(res)
+  @fact numdims(pop) --> 2
+  @fact popsize(pop) > 0 --> true
+end
+=#
+end

test/test_smoketest_bboptimize.jl

@@ -1,8 +1,8 @@
-function rosenbrock2d(x)
-  return (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2
-end
-
 facts("bboptimize smoketest") do
+  function rosenbrock2d(x)
+    return (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2
+  end
+
   for(m in keys(BlackBoxOptim.ValidMethods))
     context("testing $(m) method to ensure it works") do
       res = bboptimize(rosenbrock2d; Method = m,