import random
from NiaPy.benchmarks.utility import Utility
__all__ = ['HybridBatAlgorithm']
[docs]class HybridBatAlgorithm:
r"""Implementation of Hybrid bat algorithm.
**Algorithm:** Hybrid bat algorithm
**Date:** 2018
**Author:** Grega Vrbancic
**License:** MIT
**Reference paper:**
Fister Jr., Iztok and Fister, Dusan and Yang, Xin-She.
"A Hybrid Bat Algorithm". Elektrotehniski vestnik, 2013. 1-7.
"""
def __init__(self, D, NP, nFES, A, r, F, CR, Qmin, Qmax, benchmark):
r"""**__init__(self, D, NP, nFES, A, r, Qmin, Qmax, benchmark)**.
Arguments:
D {integer} -- dimension of problem
NP {integer} -- population size
nFES {integer} -- number of function evaluations
A {decimal} -- loudness
r {decimal} -- pulse rate
Qmin {decimal} -- minimum frequency
Qmax {decimal } -- maximum frequency
benchmark {object} -- benchmark implementation object
Raises:
TypeError -- Raised when given benchmark function which does not exists.
"""
self.benchmark = Utility().get_benchmark(benchmark)
self.D = D # dimension
self.NP = NP # population size
self.nFES = nFES # number of function evaluations
self.A = A # loudness
self.r = r # pulse rate
self.F = F # scaling factor
self.CR = CR # crossover rate
self.Qmin = Qmin # frequency min
self.Qmax = Qmax # frequency max
self.Lower = self.benchmark.Lower # lower bound
self.Upper = self.benchmark.Upper # upper bound
self.eval_flag = True # evaluations flag
self.Fun = self.benchmark.function()
self.f_min = 0.0 # minimum fitness
self.Lb = [0] * self.D # lower bound
self.Ub = [0] * self.D # upper bound
self.Q = [0] * self.NP # frequency
self.v = [[0 for _i in range(self.D)]
for _j in range(self.NP)] # velocity
self.Sol = [[0 for _i in range(self.D)] for _j in range(
self.NP)] # population of solutions
self.Fitness = [0] * self.NP # fitness
self.best = [0] * self.D # best solution
self.evaluations = 0 # evaluations counter
[docs] def best_bat(self):
"""Find the best bat."""
i = 0
j = 0
for i in range(self.NP):
if self.Fitness[i] < self.Fitness[j]:
j = i
for i in range(self.D):
self.best[i] = self.Sol[j][i]
self.f_min = self.Fitness[j]
[docs] def eval_true(self):
"""Check evauations."""
if self.evaluations == self.nFES:
self.eval_flag = False
[docs] def init_bat(self):
"""Initialize population."""
for i in range(self.D):
self.Lb[i] = self.Lower
self.Ub[i] = self.Upper
for i in range(self.NP):
self.Q[i] = 0
for j in range(self.D):
rnd = random.uniform(0, 1)
self.v[i][j] = 0.0
self.Sol[i][j] = self.Lb[j] + (self.Ub[j] - self.Lb[j]) * rnd
self.Fitness[i] = self.Fun(self.D, self.Sol[i])
self.evaluations = self.evaluations + 1
self.best_bat()
[docs] @classmethod
def simplebounds(cls, val, lower, upper):
"""Keep it within bounds."""
if val < lower:
val = lower
if val > upper:
val = upper
return val
[docs] def move_bat(self):
"""Move bats in search space."""
self.init_bat()
S = [[self.best[i] for i in range(self.D)] for j in range(self.NP)]
while self.eval_flag is not False:
for i in range(self.NP):
rnd = random.uniform(0, 1)
self.Q[i] = self.Qmin + (self.Qmax - self.Qmin) * rnd
j = None
for j in range(self.D):
self.v[i][j] = self.v[i][j] + (self.Sol[i][j] -
self.best[j]) * self.Q[i]
S[i][j] = self.Sol[i][j] + self.v[i][j]
S[i][j] = self.simplebounds(S[i][j], self.Lb[j],
self.Ub[j])
rnd = random.random()
if rnd > self.r:
nums = random.sample(range(0, self.NP), 4) # DE step
for j in range(self.D):
if random.random() < self.CR:
S[i][j] = self.best[j] + self.F * \
(S[nums[0]][j] + S[nums[1]][j] - S[nums[2]][j] - S[nums[3]][j])
S[i][j] = self.simplebounds(
S[i][j], self.Lb[j], self.Ub[j])
self.eval_true()
if self.eval_flag is not True:
break
Fnew = self.Fun(self.D, S[i])
self.evaluations = self.evaluations + 1
rnd = random.random()
if (Fnew <= self.Fitness[i]) and (rnd < self.A):
for j in range(self.D):
self.Sol[i][j] = S[i][j]
self.Fitness[i] = Fnew
if Fnew <= self.f_min:
for j in range(self.D):
self.best[j] = S[i][j]
self.f_min = Fnew
return self.f_min
[docs] def run(self):
"""Run."""
return self.move_bat()