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