def de_solve(CF): solver = DifferentialEvolutionSolver(ND, NP) solver.enable_signal_handler() stepmon = Monitor() solver.SetRandomInitialPoints(min=minrange, max=maxrange) solver.SetStrictRanges(min=minrange, max=maxrange) solver.SetEvaluationLimits(generations=MAX_GENERATIONS) solver.SetGenerationMonitor(stepmon) termination = ChangeOverGeneration(generations=generations) solver.Solve(CF, termination=termination, strategy=Rand1Exp, sigint_callback=plot_sol(solver)) solution = solver.Solution() return solution, stepmon
def de_solve(CF): solver = DifferentialEvolutionSolver(ND, NP) solver.enable_signal_handler() stepmon = Monitor() solver.SetRandomInitialPoints(min=minrange, max=maxrange) solver.SetStrictRanges(min=minrange, max=maxrange) solver.SetEvaluationLimits(generations=MAX_GENERATIONS) solver.SetGenerationMonitor(stepmon) termination = ChangeOverGeneration(generations=generations) solver.Solve(CF, termination=termination, strategy=Rand1Exp, \ sigint_callback = plot_sol(solver)) solution = solver.Solution() return solution, stepmon
def main(): solver = DifferentialEvolutionSolver(ND, NP) solver.SetRandomInitialPoints(min = [-100.0]*ND, max = [100.0]*ND) solver.SetEvaluationLimits(generations=MAX_GENERATIONS) solver.SetGenerationMonitor(VerboseMonitor(30)) solver.enable_signal_handler() strategy = Best1Exp #strategy = Best1Bin solver.Solve(ChebyshevCost, termination=VTR(0.01), strategy=strategy, \ CrossProbability=1.0, ScalingFactor=0.9, \ sigint_callback=plot_solution) solution = solver.Solution() return solution
def main(): solver = DifferentialEvolutionSolver(ND, NP) solver.SetRandomInitialPoints(min=[-100.0] * ND, max=[100.0] * ND) solver.SetEvaluationLimits(generations=MAX_GENERATIONS) solver.SetGenerationMonitor(VerboseMonitor(30)) solver.enable_signal_handler() strategy = Best1Exp #strategy = Best1Bin solver.Solve(ChebyshevCost, termination=VTR(0.01), strategy=strategy, \ CrossProbability=1.0, ScalingFactor=0.9, \ sigint_callback=plot_solution) solution = solver.Solution() return solution
def de_solve(CF): solver = DifferentialEvolutionSolver(ND, NP) solver.enable_signal_handler() stepmon = VerboseMonitor(10,50) minrange = [-100., -100., -100.]; maxrange = [100., 100., 100.]; solver.SetRandomInitialPoints(min = minrange, max = maxrange) solver.SetStrictRanges(min = minrange, max = maxrange) solver.SetEvaluationLimits(generations=MAX_GENERATIONS) solver.SetGenerationMonitor(stepmon) solver.Solve(CF, termination=ChangeOverGeneration(generations=300),\ CrossProbability=0.5, ScalingFactor=0.5,\ sigint_callback=plot_sol) solution = solver.Solution() return solution, stepmon
def test_griewangk(): """Test Griewangk's function, which has many local minima. Testing Griewangk: Expected: x=[0.]*10 and f=0 Using DifferentialEvolutionSolver: Solution: [ 8.87516194e-09 7.26058147e-09 1.02076001e-08 1.54219038e-08 -1.54328461e-08 2.34589663e-08 2.02809360e-08 -1.36385836e-08 1.38670373e-08 1.59668900e-08] f value: 0.0 Iterations: 4120 Function evaluations: 205669 Time elapsed: 34.4936850071 seconds Using DifferentialEvolutionSolver2: Solution: [ -2.02709316e-09 3.22017968e-09 1.55275472e-08 5.26739541e-09 -2.18490470e-08 3.73725584e-09 -1.02315312e-09 1.24680355e-08 -9.47898116e-09 2.22243557e-08] f value: 0.0 Iterations: 4011 Function evaluations: 200215 Time elapsed: 32.8412370682 seconds """ print "Testing Griewangk:" print "Expected: x=[0.]*10 and f=0" from mystic.models import griewangk as costfunc ndim = 10 lb = [-400.]*ndim ub = [400.]*ndim maxiter = 10000 seed = 123 # Re-seed for each solver to have them all start at same x0 # DifferentialEvolutionSolver print "\nUsing DifferentialEvolutionSolver:" npop = 50 random_seed(seed) from mystic.solvers import DifferentialEvolutionSolver from mystic.termination import ChangeOverGeneration as COG from mystic.termination import CandidateRelativeTolerance as CRT from mystic.termination import VTR from mystic.strategy import Rand1Bin, Best1Bin, Rand1Exp esow = Monitor() ssow = Monitor() solver = DifferentialEvolutionSolver(ndim, npop) solver.SetRandomInitialPoints(lb, ub) solver.SetStrictRanges(lb, ub) solver.SetEvaluationLimits(generations=maxiter) solver.SetEvaluationMonitor(esow) solver.SetGenerationMonitor(ssow) solver.enable_signal_handler() #term = COG(1e-10) #term = CRT() term = VTR(0.) time1 = time.time() # Is this an ok way of timing? solver.Solve(costfunc, term, strategy=Rand1Exp, \ CrossProbability=0.3, ScalingFactor=1.0) sol = solver.Solution() time_elapsed = time.time() - time1 fx = solver.bestEnergy print "Solution: ", sol print "f value: ", fx print "Iterations: ", solver.generations print "Function evaluations: ", len(esow.x) print "Time elapsed: ", time_elapsed, " seconds" assert almostEqual(fx, 0.0, tol=3e-3) # DifferentialEvolutionSolver2 print "\nUsing DifferentialEvolutionSolver2:" npop = 50 random_seed(seed) from mystic.solvers import DifferentialEvolutionSolver2 from mystic.termination import ChangeOverGeneration as COG from mystic.termination import CandidateRelativeTolerance as CRT from mystic.termination import VTR from mystic.strategy import Rand1Bin, Best1Bin, Rand1Exp esow = Monitor() ssow = Monitor() solver = DifferentialEvolutionSolver2(ndim, npop) solver.SetRandomInitialPoints(lb, ub) solver.SetStrictRanges(lb, ub) solver.SetEvaluationLimits(generations=maxiter) solver.SetEvaluationMonitor(esow) solver.SetGenerationMonitor(ssow) #term = COG(1e-10) #term = CRT() term = VTR(0.) time1 = time.time() # Is this an ok way of timing? solver.Solve(costfunc, term, strategy=Rand1Exp, \ CrossProbability=0.3, ScalingFactor=1.0) sol = solver.Solution() time_elapsed = time.time() - time1 fx = solver.bestEnergy print "Solution: ", sol print "f value: ", fx print "Iterations: ", solver.generations print "Function evaluations: ", len(esow.x) print "Time elapsed: ", time_elapsed, " seconds" assert almostEqual(fx, 0.0, tol=3e-3)