BAT.zip

# -*- coding: utf-8 -*- """ Created on Thu May 26 02:00:55 2016 @author: hossam """ import math import numpy import random import time from solution import solution def BAT(objf, lb, ub, dim, N, Max_iteration): n = N # Population size if not isinstance(lb, list): lb = [lb] * dim if not isinstance(ub, list): ub = [ub] * dim N_gen = Max_iteration # Number of generations A = 0.5 # Loudness (constant or decreasing) r = 0.5 # Pulse rate (constant or decreasing) Qmin = 0 # Frequency minimum Qmax = 2 # Frequency maximum d = dim # Number of dimensions # Initializing arrays Q = numpy.zeros(n) # Frequency v = numpy.zeros((n, d)) # Velocities Convergence_curve = [] # Initialize the population/solutions Sol = numpy.zeros((n, d)) for i in range(dim): Sol[:, i] = numpy.random.rand(n) * (ub[i] - lb[i]) + lb[i] S = numpy.zeros((n, d)) S = numpy.copy(Sol) Fitness = numpy.zeros(n) # initialize solution for the final results s = solution() print('BAT is optimizing "' + objf.__name__ + '"') # Initialize timer for the experiment timerStart = time.time() s.startTime = time.strftime("%Y-%m-%d-%H-%M-%S") # Evaluate initial random solutions for i in range(0, n): Fitness[i] = objf(Sol[i, :]) # Find the initial best solution and minimum fitness I = numpy.argmin(Fitness) best = Sol[I, :] fmin = min(Fitness) # Main loop for t in range(0, N_gen): # Loop over all bats(solutions) for i in range(0, n): Q[i] = Qmin + (Qmin - Qmax) * random.random() v[i, :] = v[i, :] + (Sol[i, :] - best) * Q[i] S[i, :] = Sol[i, :] + v[i, :] # Check boundaries for j in range(d): Sol[i, j] = numpy.clip(Sol[i, j], lb[j], ub[j]) # Pulse rate if random.random() > r: S[i, :] = best + 0.001 * numpy.random.randn(d) # Evaluate new solutions Fnew = objf(S[i, :]) # Update if the solution improves if (Fnew <= Fitness[i]) and (random.random() < A): Sol[i, :] = numpy.copy(S[i, :]) Fitness[i] = Fnew # Update the current best solution if Fnew <= fmin: best = numpy.copy(S[i, :]) fmin = Fnew # update convergence curve Convergence_curve.append(fmin) if t % 1 == 0: print(["At iteration " + str(t) + " the best fitness is " + str(fmin)]) timerEnd = time.time() s.endTime = time.strftime("%Y-%m-%d-%H-%M-%S") s.executionTime = timerEnd - timerStart s.convergence = Convergence_curve s.optimizer = "BAT" s.objfname = objf.__name__ return s