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"""Grey wolf optimizer. |
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Date: 11.2.2018 |
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Author : Iztok Fister Jr. |
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License: MIT |
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Reference paper: Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. |
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"Grey wolf optimizer." Advances in engineering software 69 (2014): 46-61. |
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#& Grey Wold Optimizer (GWO) source codes version 1.0 (MATLAB) |
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TODO: Validation must be conducted! More tests are required! |
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""" |
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import random |
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__all__ = ['GreyWolfOptimizer'] |
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class GreyWolfOptimizer(object): |
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# pylint: disable=too-many-instance-attributes |
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View Code Duplication |
def __init__(self, D, NP, nFES, Lower, Upper, function): |
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self.D = D # dimension of the problem |
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self.NP = NP # population size; number of search agents |
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self.nFES = nFES # number of function evaluations |
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self.Lower = Lower # lower bound |
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self.Upper = Upper # upper bound |
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self.Fun = function |
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self.Positions = [[0 for _i in range(self.D)] # positions of search agents |
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for _j in range(self.NP)] |
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self.evaluations = 0 # evaluations counter |
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# TODO: "-inf" is in the case of maximization problems |
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self.Alpha_pos = [0] * self.D # init of alpha |
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self.Alpha_score = float("inf") |
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self.Beta_pos = [0] * self.D # init of beta |
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self.Beta_score = float("inf") |
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self.Delta_pos = [0] * self.D # init of delta |
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self.Delta_score = float("inf") |
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def init(self): |
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# initialization of positions |
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for i in range(self.NP): |
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for j in range(self.D): |
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self.Positions[i][j] = random.random( |
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) * (self.Upper - self.Lower) + self.Lower |
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def bounds(self, position): |
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for i in range(self.D): |
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if position[i] < self.Lower: |
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position[i] = self.Lower |
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if position[i] > self.Upper: |
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position[i] = self.Upper |
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return position |
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def move(self): |
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self.init() |
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while True: |
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if self.evaluations == self.nFES: |
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break |
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for i in range(self.NP): |
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self.Positions[i] = self.bounds(self.Positions[i]) |
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Fit = Fun(self.D, self.Positions[i]) |
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self.evaluations = self.evaluations + 1 |
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if Fit < self.Alpha_score: |
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self.Alpha_score = Fit |
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self.Alpha_pos = self.Positions[i] |
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if ((Fit > self.Alpha_score) and (Fit < self.Beta_score)): |
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self.Beta_score = Fit |
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self.Beta_pos = self.Positions[i] |
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if ((Fit > self.Alpha_score) and (Fit > self.Beta_score) and (Fit < self.Delta_score)): |
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self.Delta_score = Fit |
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self.Delta_pos = self.Positions[i] |
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a = 2 - self.evaluations * ((2) / self.nFES) |
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for i in range(self.NP): |
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for j in range(self.D): |
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r1 = random.random() |
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r2 = random.random() |
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A1 = 2 * a * r1 - a |
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C1 = 2 * r2 |
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D_alpha = abs( |
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C1 * self.Alpha_pos[j] - self.Positions[i][j]) |
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X1 = self.Alpha_pos[j] - A1 * D_alpha |
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r1 = random.random() |
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r2 = random.random() |
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A2 = 2 * a * r1 - a |
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C2 = 2 * r2 |
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D_beta = abs(C2 * self.Beta_pos[j] - self.Positions[i][j]) |
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X2 = self.Beta_pos[j] - A2 * D_beta |
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r1 = random.random() |
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r2 = random.random() |
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A3 = 2 * a * r1 - a |
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C3 = 2 * r2 |
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D_delta = abs( |
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C3 * self.Delta_pos[j] - self.Positions[i][j]) |
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X3 = self.Delta_pos[j] - A3 * D_delta |
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self.Positions[i][j] = (X1 + X2 + X3) / 3 |
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return self.Alpha_score |
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NiaPy
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