def solveOpt(int_domain,J,x,model,u0): def objfun(u,**kwargs): # 1) extract paraeters int_domain = kwargs['int_domain'] J = kwargs['J'] x = kwargs['x'] model = kwargs['model'] # 2) define objective function f = np.trapz(int_domain,J * model.pf(int_domain,u,x)) g = [0]*2 # 3) budget constraint g[1] = u.sum() - 1 # 4) VaR constarint W = model.W sigmaMax = model.VaR / norm.ppf(1-model.alpha) g[0] = -sigmaMax + np.sqrt(W.dot(u).dot(u)) fail = 0 return f,g,fail opt_prob = Optimization('test problem',objfun) opt_prob.addObj('f') opt_prob.addCon('budget const','e') opt_prob.addCon('VaR const','i') opt_prob.addVarGroup('u',model.M,'c',lower=np.zeros(model.M), upper=np.ones(model.M),value=u0) print opt_prob slsqp = SLSQP() slsqp.setOption('IPRINT',-1) slsqp(opt_prob,sens_type='FD',int_domain=int_domain,J=J,x=x,model=model) print opt_prob.solution(0)
def traj_optim_static(paths, tree): path, envs, modes, mnps = paths guard_index = [0] n = len(modes) v_init = np.zeros((n, 3)) for i in range(1, n): if not np.all(modes[i] == modes[i - 1]): guard_index.append(i) elif len(envs[i]) != 0: if not envs[i][0].is_same(envs[i - 1][0]): guard_index.append(i) elif not (mnps[i][0].is_same(mnps[i - 1][0]) and mnps[i][1].is_same(mnps[i - 1][1])): # manipulator change guard_index.append(i) g_v = np.identity(3) g_v[0:2, 0:2] = config2trans(path[i - 1])[0:2, 0:2] v_init[i - 1] = np.dot(g_v.T, np.array(path[i]) - np.array(path[i - 1])) #guard_index.append(len(modes)-1) guard_index = np.unique(guard_index) Gs = dict() hs = dict() As = dict() bs = dict() for i in range(len(path)): G, h, A, b = contact_mode_constraints(path[i], mnps[i], envs[i], modes[i], tree.world, tree.mnp_mu, tree.env_mu, tree.mnp_fn_max) gid = np.any(G[:, 0:3], axis=1) aid = np.any(A[:, 0:3], axis=1) Gs[i] = G[gid, 0:3] hs[i] = h[gid].flatten() As[i] = A[aid, 0:3] bs[i] = b[aid].flatten() modeconstraints = (Gs, hs, As, bs) q_goal = np.array(tree.x_goal) opt_prob = Optimization('Trajectory Optimization', obj_fun) x_init = np.hstack((np.array(path).flatten(), v_init.flatten())) cs = constraints(x_init, path, Gs, hs, As, bs, guard_index) opt_prob.addVarGroup('x', n * 6, 'c', value=x_init, lower=-10, upper=10) opt_prob.addObj('f') opt_prob.addConGroup('g', len(cs), 'i', lower=0.0, upper=10000.0) print(opt_prob) slsqp = SLSQP() #slsqp.setOption('IPRINT', -1) slsqp(opt_prob, sens_type='FD', goal=q_goal, path=path, modecons=modeconstraints, guard_index=guard_index) print(opt_prob.solution(0)) qs = [opt_prob.solution(0)._variables[i].value for i in range(n * 3)] return qs
def solveOpt(int_domain, J, a, model, u0, sign): ''' INPUT: int_domain = J = a = model = u0 = sign = OUTPUT: opt_prob = ''' def objfun(u, **kwargs): '''objfun defines optimization problem using the pyOpt sintax''' # 1) extract paraeters int_domain = kwargs['int_domain'] J = kwargs['J'] x = kwargs['a'] model = kwargs['model'] sign = kwargs['sign'] # 2) define objective function and constraints funz = np.trapz(x=int_domain, y=J * model.pf(int_domain, u, x)) g = [] fail = 0 return sign * funz, g, fail opt_prob = Optimization('ODAA problem', objfun) opt_prob.addObj('funz') solver = SLSQP() # choose the solver solver.setOption('IPRINT', -1) opt_prob.addVar('u', 'c', lower=-1, upper=1, value=u0) #print opt_prob # print optimization problem solver(opt_prob, int_domain=int_domain, J=J, a=a, model=model, sign=sign) #print opt_prob.solution(0) # print solution return opt_prob
def solveOpt(int_domain, J, a, model, u0, sign): ''' INPUT: int_domain = J = a = model = u0 = sign = OUTPUT: opt_prob = ''' def objfun(u, **kwargs): '''objfun defines the objective function and the constraints (equality and inequality) of the optiization problem''' # 1) extract paraeters int_domain = kwargs['int_domain'] J = kwargs['J'] x = kwargs['a'] model = kwargs['model'] sign = kwargs['sign'] # 2) define objective function funz = np.trapz(x=int_domain, y=J * model.pf(int_domain, u, x)) g = [0] * 2 # 3) budget constraint g[0] = u.sum() - 1 # 4) VaR constarint W = model.W sigmaMax = model.VaR / norm.ppf(1 - model.alpha) g[1] = -sigmaMax + np.sqrt(W.dot(u).dot(u)) fail = 0 return sign * funz, g, fail opt_prob = Optimization('ODAA problem', objfun) opt_prob.addObj('funz') opt_prob.addCon('budget const', 'e') opt_prob.addCon('VaR const', 'i') slsqp = SLSQP() # instantiate Optimizer slsqp.setOption('IPRINT', -1) opt_prob.addVarGroup('u', model.M, 'c', lower=np.zeros(model.M), upper=np.ones(model.M), value=u0) #print opt_prob # print optimization problem slsqp(opt_prob, sens_type='FD', int_domain=int_domain, J=J, a=a, model=model, sign=sign) #print opt_prob.solution(0) # print solution return opt_prob
def optimize(k, w1, w2): # Physical problem rho = 0.2836 # lb/in^3 L = 5.0 # in P = 25000.0 # lb E = 30.0e6 # psi ys = 36260.0 # psi fs = 1.5 dtruss = TwoBarTruss(rho, L, P, E, ys, fs) struss = StochasticTwoBarTruss(dtruss) # Optimization Problem optproblem = TwoBarTrussOpt(MPI.COMM_WORLD, struss, k, w1, w2) opt_prob = Optimization(args.logfile, optproblem.evalObjCon) # Add functions opt_prob.addObj('weight') opt_prob.addCon('buckling-bar1', type='i') opt_prob.addCon('failure-bar1' , type='i') opt_prob.addCon('failure-bar2' , type='i') # Add variables opt_prob.addVar('area-1', type='c', value= 1.5, lower= 1.5, upper= 1.5) opt_prob.addVar('area-2', type='c', value= 1.5, lower= 1.5, upper= 1.5) opt_prob.addVar('height', type='c', value= 4.0, lower= 4.0, upper= 10.0) # Optimization algorithm if args.algorithm == 'ALGENCAN': opt = ALGENCAN() opt.setOption('iprint',2) opt.setOption('epsfeas',1e-6) opt.setOption('epsopt',1e-6) else: opt = SLSQP(pll_type='POA') opt.setOption('MAXIT',999) opt(opt_prob, sens_type=optproblem.evalObjConGradient, disp_opts=True, store_hst=True, hot_start=False) if optproblem.comm.Get_rank() ==0: print opt_prob.solution(0) opt_prob.write2file(disp_sols=True) x = optproblem.x_hist[-1] f = optproblem.fvals[0] print 'x', x print 'f', f return x, f
def optimize(self, options=[]): # Set SLSQP as the optimizer self.opt = SLSQP() # Set optimization options if (len(options) > 0): for ii in range(len(options)): self.opt.setOption(options.keys()[ii], options.values()[ii]) # Print the Optimizer Options print("----------------------------------------") print("----------------------------------------") print("SLSQP Optimizer options:") print(self.opt.options) # Get optimized controller self.opt(self.opt_prob, sens_step=1e-6) print(self.opt_prob.solution(0)) a = self.opt_prob.solution(0) for ii in range(self.building.policy.shape[1]): self.building.policy[0, ii] = a.getVar(ii).value return self.building.policy
# Add functions opt_prob.addObj('weight') opt_prob.addCon('buckling-bar1', type='i') opt_prob.addCon('failure-bar1', type='i') opt_prob.addCon('failure-bar2', type='i') # Add variables opt_prob.addVar('area-1', type='c', value=1.0, lower=1.0e-3, upper=2.0) opt_prob.addVar('area-2', type='c', value=1.0, lower=1.0e-3, upper=2.0) opt_prob.addVar('height', type='c', value=4.0, lower=4.0, upper=10.0) # Optimization algorithm if args.algorithm == 'ALGENCAN': opt = ALGENCAN() opt.setOption('iprint', 2) opt.setOption('epsfeas', 1e-6) opt.setOption('epsopt', 1e-6) else: opt = SLSQP(pll_type='POA') opt.setOption('MAXIT', 999) opt(opt_prob, sens_type=optproblem.evalObjConGradient, disp_opts=True, store_hst=True, hot_start=False) if optproblem.comm.Get_rank() == 0: print opt_prob.solution(0) opt_prob.write2file(disp_sols=True)
(x[1] - b[i])**2))) #end g = [0.0] * 1 g[0] = 20.04895 - (x[0] + 2.0)**2 - (x[1] + 1.0)**2 fail = 0 return f, g, fail # ============================================================================= # # ============================================================================= opt_prob = Optimization('Langermann Function 11', objfunc) opt_prob.addVar('x1', 'c', lower=-2.0, upper=10.0, value=8.0) opt_prob.addVar('x2', 'c', lower=-2.0, upper=10.0, value=8.0) opt_prob.addObj('f') opt_prob.addCon('g', 'i') print(opt_prob) # Global Optimization nsga2 = NSGA2() nsga2(opt_prob) print(opt_prob.solution(0)) # Local Optimization Refinement slsqp = SLSQP() slsqp(opt_prob.solution(0)) print(opt_prob.solution(0).solution(0))
def main(): ########################################### # Define some values ########################################### n_blades = 2 n_elements = 10 radius = unit_conversion.in2m(9.6) / 2 root_cutout = 0.1 * radius dy = float(radius - root_cutout) / n_elements dr = float(1) / n_elements y = root_cutout + dy * np.arange(1, n_elements + 1) r = y / radius pitch = 0.0 airfoils = (('SDA1075_494p', 0.0, 1.0), ) allowable_Re = [ 1000000., 500000., 250000., 100000., 90000., 80000., 70000., 60000., 50000., 40000., 30000., 20000., 10000. ] vehicle_weight = 12.455 max_chord = 0.3 max_chord_tip = 5. alt = 0 tip_loss = True mach_corr = False # Forward flight parameters v_inf = 4. # m/s alpha0 = 0.0454 # Starting guess for trimmed alpha in radians n_azi_elements = 5 # Mission times time_in_hover = 300. # Time in seconds time_in_ff = 500. mission_time = [time_in_hover, time_in_ff] Cl_tables = {} Cd_tables = {} Clmax = {} # Get lookup tables if any(airfoil[0] != 'simple' for airfoil in airfoils): for airfoil in airfoils: Cl_table, Cd_table, Clmax = aero_coeffs.create_Cl_Cd_table( airfoil[0]) Cl_tables[airfoil[0]] = Cl_table Cd_tables[airfoil[0]] = Cd_table Clmax[airfoil[0]] = Clmax # Create list of Cl functions. One for each Reynolds number. Cl_tables (and Cd_tables) will be empty for the # 'simple' case, therefore this will be skipped for the simple case. For the full table lookup case this will be # skipped because allowable_Re will be empty. Cl_funs = {} Cd_funs = {} lift_curve_info_dict = {} if Cl_tables and allowable_Re: Cl_funs = dict( zip(allowable_Re, [ aero_coeffs.get_Cl_fun(Re, Cl_tables[airfoils[0][0]], Clmax[airfoils[0][0]][Re]) for Re in allowable_Re ])) Cd_funs = dict( zip(allowable_Re, [ aero_coeffs.get_Cd_fun(Re, Cd_tables[airfoils[0][0]]) for Re in allowable_Re ])) lift_curve_info_dict = aero_coeffs.create_liftCurveInfoDict( allowable_Re, Cl_tables[airfoils[0][0]]) ########################################### # Set design variable bounds ########################################### # Hover opt 500 gen, 1000 pop, 12.455 N weight, 9.6 in prop chord = np.array([ 0.11923604, 0.2168746, 0.31540216, 0.39822882, 0.42919, 0.35039799, 0.3457828, 0.28567224, 0.23418368, 0.13502483 ]) twist = np.array([ 0.45316866, 0.38457724, 0.38225075, 0.34671967, 0.33151445, 0.28719111, 0.25679667, 0.25099005, 0.19400679, 0.10926302 ]) omega = 3811.03596674 * 2 * np.pi / 60 original = (omega, chord, twist) dtwist = np.array( [twist[i + 1] - twist[i] for i in xrange(len(twist) - 1)]) dchord = np.array( [chord[i + 1] - chord[i] for i in xrange(len(chord) - 1)]) twist0 = twist[0] chord0 = chord[0] omega_start = omega dtwist_start = dtwist dchord_start = dchord twist0_start = twist0 chord0_start = chord0 omega_lower = 2000 * 2 * np.pi / 60 omega_upper = 8000.0 * 2 * np.pi / 60 twist0_lower = 0. * 2 * np.pi / 360 twist0_upper = 60. * 2 * np.pi / 360 chord0_upper = 0.1198 chord0_lower = 0.05 dtwist_lower = -10.0 * 2 * np.pi / 360 dtwist_upper = 10.0 * 2 * np.pi / 360 dchord_lower = -0.1 dchord_upper = 0.1 opt_prob = Optimization('Mission Simulator', objfun) opt_prob.addVar('omega_h', 'c', value=omega_start, lower=omega_lower, upper=omega_upper) opt_prob.addVar('twist0', 'c', value=twist0_start, lower=twist0_lower, upper=twist0_upper) opt_prob.addVar('chord0', 'c', value=chord0_start, lower=chord0_lower, upper=chord0_upper) opt_prob.addVarGroup('dtwist', n_elements - 1, 'c', value=dtwist_start, lower=dtwist_lower, upper=dtwist_upper) opt_prob.addVarGroup('dchord', n_elements - 1, 'c', value=dchord_start, lower=dchord_lower, upper=dchord_upper) opt_prob.addObj('f') opt_prob.addCon('thrust', 'i') opt_prob.addCon('c_tip', 'i') opt_prob.addConGroup('c_lower', n_elements, 'i') opt_prob.addConGroup('c_upper', n_elements, 'i') print opt_prob slsqp = SLSQP() slsqp.setOption('IPRINT', 1) slsqp.setOption('MAXIT', 1000) slsqp.setOption('ACC', 1e-8) fstr, xstr, inform = slsqp(opt_prob, sens_type='FD', n_blades=n_blades, radius=radius, dy=dy, dr=dr, y=y, r=r, pitch=pitch, airfoils=airfoils, vehicle_weight=vehicle_weight, max_chord=max_chord, tip_loss=tip_loss, mach_corr=mach_corr, Cl_funs=Cl_funs, Cd_funs=Cd_funs, Cl_tables=Cl_tables, Cd_tables=Cd_tables, allowable_Re=allowable_Re, alt=alt, v_inf=v_inf, alpha0=alpha0, mission_time=mission_time, n_azi_elements=n_azi_elements, lift_curve_info_dict=lift_curve_info_dict, max_chord_tip=max_chord_tip) print opt_prob.solution(0) # pop_size = 300 # max_gen = 500 # opt_method = 'nograd' # nsga2 = NSGA2() # nsga2.setOption('PrintOut', 2) # nsga2.setOption('PopSize', pop_size) # nsga2.setOption('maxGen', max_gen) # nsga2.setOption('pCross_real', 0.85) # nsga2.setOption('xinit', 1) # fstr, xstr, inform = nsga2(opt_prob, n_blades=n_blades, radius=radius, dy=dy, dr=dr, y=y, r=r, pitch=pitch, # airfoils=airfoils, vehicle_weight=vehicle_weight, max_chord=max_chord, tip_loss=tip_loss, # mach_corr=mach_corr, Cl_funs=Cl_funs, Cd_funs=Cd_funs, Cl_tables=Cl_tables, # Cd_tables=Cd_tables, allowable_Re=allowable_Re, opt_method=opt_method, alt=alt, # v_inf=v_inf, alpha0=alpha0, mission_time=mission_time, n_azi_elements=n_azi_elements, # pop_size=pop_size, max_gen=max_gen, lift_curve_info_dict=lift_curve_info_dict, # max_chord_tip=max_chord_tip) # print opt_prob.solution(0) # opt_method = 'nograd' # xstart_alpso = np.concatenate((np.array([omega_start, twist0_start, chord0_start]), dtwist_start, dchord_start)) # alpso = ALPSO() # alpso.setOption('xinit', 0) # alpso.setOption('SwarmSize', 200) # alpso.setOption('maxOuterIter', 100) # alpso.setOption('stopCriteria', 0) # fstr, xstr, inform = alpso(opt_prob, xstart=xstart_alpso, n_blades=n_blades, n_elements=n_elements, # root_cutout=root_cutout, radius=radius, dy=dy, dr=dr, y=y, r=r, pitch=pitch, # airfoils=airfoils, thrust=thrust, max_chord=max_chord, tip_loss=tip_loss, # mach_corr=mach_corr, Cl_funs=Cl_funs, Cd_funs=Cd_funs, Cl_tables=Cl_tables, # Cd_tables=Cd_tables, allowable_Re=allowable_Re, opt_method=opt_method) # print opt_prob.solution(0) def get_performance(o, c, t): chord_meters = c * radius prop = propeller.Propeller(t, chord_meters, radius, n_blades, r, y, dr, dy, airfoils=airfoils, Cl_tables=Cl_tables, Cd_tables=Cd_tables) quad = quadrotor.Quadrotor(prop, vehicle_weight) ff_kwargs = { 'propeller': prop, 'pitch': pitch, 'n_azi_elements': n_azi_elements, 'allowable_Re': allowable_Re, 'Cl_funs': Cl_funs, 'Cd_funs': Cd_funs, 'tip_loss': tip_loss, 'mach_corr': mach_corr, 'alt': alt, 'lift_curve_info_dict': lift_curve_info_dict } trim0 = np.array([alpha0, o]) alpha_trim, omega_trim, converged = trim.trim(quad, v_inf, trim0, ff_kwargs) T_ff, H_ff, P_ff = bemt.bemt_forward_flight( quad, pitch, omega_trim, alpha_trim, v_inf, n_azi_elements, alt=alt, tip_loss=tip_loss, mach_corr=mach_corr, allowable_Re=allowable_Re, Cl_funs=Cl_funs, Cd_funs=Cd_funs, lift_curve_info_dict=lift_curve_info_dict) dT_h, P_h = bemt.bemt_axial(prop, pitch, o, allowable_Re=allowable_Re, Cl_funs=Cl_funs, Cd_funs=Cd_funs, tip_loss=tip_loss, mach_corr=mach_corr, alt=alt) return sum(dT_h), P_h, T_ff, P_ff, alpha_trim, omega_trim omega = xstr[0] twist0 = xstr[1] chord0 = xstr[2] dtwist = xstr[3:3 + len(r) - 1] dchord = xstr[3 + len(r) - 1:] twist = calc_twist_dist(twist0, dtwist) chord = calc_chord_dist(chord0, dchord) print "chord = " + repr(chord) print "twist = " + repr(twist) # twist_base = calc_twist_dist(twist0_base, dtwist_base) # chord_base = calc_chord_dist(chord0_base, dchord_base) perf_opt = get_performance(omega, chord, twist) perf_orig = get_performance(original[0], original[1], original[2]) print "omega_orig = " + str(original[0]) print "Hover Thrust of original = " + str(perf_orig[0]) print "Hover Power of original = " + str(perf_orig[1]) print "FF Thrust of original = " + str(perf_orig[2]) print "FF Power of original = " + str(perf_orig[3]) print "Trim original (alpha, omega) = (%f, %f)" % (perf_orig[4], perf_orig[5]) print "omega = " + str(omega * 60 / 2 / np.pi) print "Hover Thrust of optimized = " + str(perf_opt[0]) print "Hover Power of optimized = " + str(perf_opt[1]) print "FF Thrust of optimized = " + str(perf_opt[2]) print "FF Power of optimized = " + str(perf_opt[3]) print "Trim optimized (alpha, omega) = (%f, %f)" % (perf_opt[4], perf_opt[5]) # print "Omega base = " + str(omega_start*60/2/np.pi) # print "Thrust of base = " + str(sum(perf_base[0])) # print "Power of base = " + str(sum(perf_base[1])) # plt.figure(1) plt.plot(r, original[1], '-b') plt.plot(r, chord, '-r') plt.xlabel('radial location') plt.ylabel('c/R') plt.legend(['start', 'opt']) plt.figure(2) plt.plot(r, original[2] * 180 / np.pi, '-b') plt.plot(r, twist * 180 / np.pi, '-r') plt.xlabel('radial location') plt.ylabel('twist') plt.legend(['start', 'opt']) plt.show()
def optimize_twist(**k): omega = k['omega'] omega_lower = k['omega_lower'] omega_upper = k['omega_upper'] twist0 = k['twist0'] twist0_lower = k['twist0_lower'] twist0_upper = k['twist0_upper'] n_elements = k['n_elements'] dtwist = k['dtwist'] dtwist_lower = k['dtwist_lower'] dtwist_upper = k['dtwist_upper'] opt_prob_fc = Optimization('Rotor in Hover w/ Fixed Chord', objfun_optimize_twist) opt_prob_fc.addVar('omega', 'c', value=omega, lower=omega_lower, upper=omega_upper) opt_prob_fc.addVar('twist0', 'c', value=twist0, lower=twist0_lower, upper=twist0_upper) opt_prob_fc.addVarGroup('dtwist', n_elements - 1, 'c', value=dtwist, lower=dtwist_lower, upper=dtwist_upper) opt_prob_fc.addObj('f') opt_prob_fc.addCon('thrust', 'i') n_blades = k['n_blades'] root_cutout = k['root_cutout'] radius = k['radius'] dy = k['dy'] dr = k['dr'] y = k['y'] r = k['r'] pitch = k['pitch'] airfoils = k['airfoils'] thrust = k['thrust'] chord = k['chord'] allowable_Re = k['allowable_Re'] Cl_tables = k['Cl_tables'] Cd_tables = k['Cd_tables'] Cl_funs = k['Cl_funs'] Cd_funs = k['Cd_funs'] tip_loss = k['tip_loss'] mach_corr = k['mach_corr'] alt = k['alt'] # Routine for optimizing twist with a constant chord slsqp2 = SLSQP() slsqp2.setOption('IPRINT', 1) slsqp2.setOption('MAXIT', 200) slsqp2.setOption('ACC', 1e-7) fstr, xstr, inform = slsqp2(opt_prob_fc, sens_type='FD', n_blades=n_blades, n_elements=n_elements, root_cutout=root_cutout, radius=radius, dy=dy, dr=dr, y=y, r=r, pitch=pitch, airfoils=airfoils, thrust=thrust, tip_loss=tip_loss, mach_corr=mach_corr, omega=omega, chord=chord, allowable_Re=allowable_Re, Cl_tables=Cl_tables, Cd_tables=Cd_tables, Cl_funs=Cl_funs, Cd_funs=Cd_funs, alt=alt) return fstr, xstr
config, feasible_area, attraction_center=(0.5 * (site_x_start + site_x_end), 0.5 * (site_y_start + site_y_end))) distance_constraint = get_minimum_distance_constraint_func(config) constraints = [feasible_constraint, distance_constraint] # Place some turbines config.set_site_dimensions(site_x_start, site_x_end, site_y_start, site_y_end) deploy_turbines(config, nx=8, ny=2, friction=10.5) config.info() rf = ReducedFunctional(config) #m0 = rf.initial_control() #rf.update_turbine_cache(m0) #File("turbines.pvd") << config.turbine_cache.cache["turbine_field"] #import sys; sys.exit() # Load checkpoints if desired by the user if len(sys.argv) > 1 and sys.argv[1] == "--from-checkpoint": rf.load_checkpoint("checkpoint") parameters['form_compiler'][ 'cpp_optimize_flags'] = '-O3 -ffast-math -march=native' nlp, grad = rf.pyopt_problem(constraints=constraints) slsqp = SLSQP(options={"MAXIT": 300}) res = slsqp(nlp, sens_type=grad)
def optimize(self, options): # Set max number of optimization iterations maxIter = 1 if (len(options) > 0): maxIter = options["MAXIT"] # Log costs and constraints for all internal iterations self.costs = np.zeros((maxIter + 1, 1)) self.constraints = np.zeros((maxIter + 1, self.ncons)) self.policies = np.zeros((maxIter + 1, self.nvars)) # GP_SS process initPolicy = self.building.policy.copy() for ii in range(maxIter): # Train GP state-space models kernel = GPy.kern.Matern52(self.X.shape[1], ARD=False) for jj in range(0, len(self.states)): if (self.X.shape[0] > self.num_inducing): print("Using Sparse GP Model...") dynModel = GPy.models.SparseGPRegression( self.X, self.Y[:, jj].reshape(self.Y.shape[0], 1), kernel, num_inducing=self.num_inducing) self.dynModels.append(dynModel.copy()) else: print("Using Full GP Model...") dynModel = GPy.models.GPRegression( self.X, self.Y[:, jj].reshape(self.Y.shape[0], 1), kernel) dynModel.optimize_restarts(num_restarts=2) dynModel.optimize('bfgs', messages=True, max_iters=5000) self.dynModels.append(dynModel.copy()) print(self.dynModels[jj]) self.checkModelAccuracy(dynModel, self.X, self.Y[:, jj]) # Define Box Constraints (min/max values) for the control parameters boxConstraints = [] for jj in range(self.nvars): boxConstraints.append(self.controlLim) # Link to the python function calculating the cost and the constraints. Note that # this is not the actual simulation, but the propagate function self.opt_prob = Optimization('GPSS_SLSQP Constrained Problem', self.propagate) # Setupt Box Constrains in pyOpt for jj in range(self.nvars): self.opt_prob.addVar('x' + str(jj + 1), 'c', lower=boxConstraints[jj][0], upper=boxConstraints[jj][1], value=self.building.policy[0, jj]) # Setupt Cost Function in pyOpt self.opt_prob.addObj('f') # Setupt Inequality Constraints in pyOpt for jj in range(self.ncons + 1): self.opt_prob.addCon('g' + str(jj + 1), 'i') # Print the Optimization setup print("----------------------------------------") print("----------------------------------------") print("GPSS_SLSQP Optimization setup:") print(self.opt_prob) optionsSLSQP = {'ACC': 1.0e-20, 'MAXIT': 10000, 'IPRINT': 1} # Set SLSQP as the optimizer self.opt = SLSQP() # Set optimization options for jj in range(len(optionsSLSQP)): self.opt.setOption(optionsSLSQP.keys()[jj], optionsSLSQP.values()[jj]) # Print the Optimizer Options print("----------------------------------------") print("----------------------------------------") print("SLSQP Optimizer options:") print(self.opt.options) # Get optimized controller self.opt(self.opt_prob, sens_step=1e-6) print(self.opt_prob.solution(0)) a = self.opt_prob.solution(0) for jj in range(self.building.policy.shape[1]): self.building.policy[0, jj] = a.getVar(jj).value # Evaluate the optimized controller in the simulation model xa, cf, cc = self.building.simulate(self.building.policy) print("COST: = =========== " + str(np.sum(cf))) print("CONSTRAINT: = =========== " + str(np.sum(cc))) xx = xa[0:-1, ] yy = xa[1:, self.states] if (ii == 0): self.X = xx self.Y = yy else: self.X = np.concatenate((self.X, xx), axis=0) self.Y = np.concatenate((self.Y, yy), axis=0) self.costs[ii, 0] = np.sum(cf) for jj in range(self.ncons): self.constraints[ii, jj] = np.sum(cc[:, jj]) self.policies[ii, :] = self.building.policy.copy() self.building.policy = initPolicy.copy() self.policies[ii + 1, :] = self.baseline[0].copy() self.costs[ii + 1, 0] = self.baseline[1] self.constraints[ii + 1, 0] = self.baseline[2] policyIndex = self.selectBestController(self.costs, self.constraints) self.building.policy = self.policies[policyIndex, :].copy() return self.building.policy