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"""Hybrid bat algorithm. |
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Date: 2018 |
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Authors : Grega Vrbancic |
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License: MIT |
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Reference paper: Fister Jr., Iztok and Fister, Dusan and Yang, Xin-She. |
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"A Hybrid Bat Algorithm". Elektrotehniski vestnik, 2013. 1-7. |
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""" |
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import random |
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from NiaPy.benchmarks.utility import Utility |
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__all__ = ['HybridBatAlgorithm'] |
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class HybridBatAlgorithm(object): |
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# pylint: disable=too-many-instance-attributes |
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View Code Duplication |
def __init__(self, D, NP, nFES, A, r, F, CR, Qmin, Qmax, benchmark): |
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self.benchmark = Utility().get_benchmark(benchmark) |
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self.D = D # dimension |
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self.NP = NP # population size |
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self.nFES = nFES # number of function evaluations |
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self.A = A # loudness |
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self.r = r # pulse rate |
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self.F = F # scaling factor |
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self.CR = CR # crossover rate |
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self.Qmin = Qmin # frequency min |
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self.Qmax = Qmax # frequency max |
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self.Lower = self.benchmark.Lower # lower bound |
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self.Upper = self.benchmark.Upper # upper bound |
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self.eval_flag = True # evaluations flag |
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self.Fun = self.benchmark.function() |
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self.f_min = 0.0 # minimum fitness |
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self.Lb = [0] * self.D # lower bound |
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self.Ub = [0] * self.D # upper bound |
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self.Q = [0] * self.NP # frequency |
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self.v = [[0 for _i in range(self.D)] |
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for _j in range(self.NP)] # velocity |
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self.Sol = [[0 for _i in range(self.D)] for _j in range( |
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self.NP)] # population of solutions |
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self.Fitness = [0] * self.NP # fitness |
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self.best = [0] * self.D # best solution |
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self.evaluations = 0 # evaluations counter |
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def best_bat(self): |
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i = 0 |
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j = 0 |
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for i in range(self.NP): |
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if self.Fitness[i] < self.Fitness[j]: |
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j = i |
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for i in range(self.D): |
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self.best[i] = self.Sol[j][i] |
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View Code Duplication |
self.f_min = self.Fitness[j] |
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def eval_true(self): |
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"""Check evauations.""" |
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if self.evaluations == self.nFES: |
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self.eval_flag = False |
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def init_bat(self): |
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for i in range(self.D): |
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self.Lb[i] = self.Lower |
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self.Ub[i] = self.Upper |
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for i in range(self.NP): |
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self.Q[i] = 0 |
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for j in range(self.D): |
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rnd = random.uniform(0, 1) |
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self.v[i][j] = 0.0 |
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self.Sol[i][j] = self.Lb[j] + (self.Ub[j] - self.Lb[j]) * rnd |
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self.Fitness[i] = self.Fun(self.D, self.Sol[i]) |
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self.evaluations = self.evaluations + 1 |
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self.best_bat() |
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@classmethod |
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def simplebounds(cls, val, lower, upper): |
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if val < lower: |
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val = lower |
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if val > upper: |
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val = upper |
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return val |
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def move_bat(self): |
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S = [[0.0 for i in range(self.D)] for j in range(self.NP)] |
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self.init_bat() |
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while self.eval_flag is not False: |
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for i in range(self.NP): |
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rnd = random.uniform(0, 1) |
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self.Q[i] = self.Qmin + (self.Qmin - self.Qmax) * rnd |
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j = None |
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for j in range(self.D): |
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self.v[i][j] = self.v[i][j] + (self.Sol[i][j] - |
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self.best[j]) * self.Q[i] |
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S[i][j] = self.Sol[i][j] + self.v[i][j] |
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S[i][j] = self.simplebounds(S[i][j], self.Lb[j], |
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self.Ub[j]) |
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rnd = random.random() |
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if rnd > self.r: |
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if random.random() < self.CR: |
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nums = random.sample(range(0, self.NP), 3) |
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S[i][j] = S[nums[0]][j] + self.F * \ |
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(S[nums[1]][j] - S[nums[2]][j]) |
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S[i][j] = self.simplebounds(S[i][j], self.Lb[j], |
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self.Ub[j]) |
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self.eval_true() |
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if self.eval_flag is not True: |
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break |
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Fnew = self.Fun(self.D, S[i]) |
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self.evaluations = self.evaluations + 1 |
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rnd = random.random() |
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if (Fnew <= self.Fitness[i]) and (rnd < self.A): |
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for j in range(self.D): |
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self.Sol[i][j] = S[i][j] |
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self.Fitness[i] = Fnew |
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if Fnew <= self.f_min: |
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for j in range(self.D): |
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self.best[j] = S[i][j] |
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self.f_min = Fnew |
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return self.f_min |
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def run(self): |
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return self.move_bat() |
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