146 lines
4.3 KiB
Python
146 lines
4.3 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Mon May 16 00:27:50 2016
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@author: Hossam Faris
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"""
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import random
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import numpy
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import math
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from .solution import solution
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import time
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def GWO(objf, lb, ub, dim, SearchAgents_no, Max_iter):
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# Max_iter=1000
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# lb=-100
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# ub=100
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# dim=30
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# SearchAgents_no=5
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# initialize alpha, beta, and delta_pos
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Alpha_pos = numpy.zeros(dim)
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Alpha_score = float("inf")
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Beta_pos = numpy.zeros(dim)
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Beta_score = float("inf")
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Delta_pos = numpy.zeros(dim)
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Delta_score = float("inf")
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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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# Initialize the positions of search agents
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Positions = numpy.zeros((SearchAgents_no, dim))
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for i in range(dim):
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Positions[:, i] = (
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numpy.random.uniform(0, 1, SearchAgents_no) * (ub[i] - lb[i]) + lb[i]
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)
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Convergence_curve = numpy.zeros(Max_iter)
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s = solution()
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# Loop counter
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print('GWO is optimizing "' + objf.__name__ + '"')
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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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# Main loop
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for l in range(0, Max_iter):
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for i in range(0, SearchAgents_no):
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# Return back the search agents that go beyond the boundaries of the search space
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for j in range(dim):
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Positions[i, j] = numpy.clip(Positions[i, j], lb[j], ub[j])
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# Calculate objective function for each search agent
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fitness = objf(Positions[i, :])
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# Update Alpha, Beta, and Delta
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if fitness < Alpha_score:
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Delta_score = Beta_score # Update delte
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Delta_pos = Beta_pos.copy()
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Beta_score = Alpha_score # Update beta
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Beta_pos = Alpha_pos.copy()
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Alpha_score = fitness
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# Update alpha
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Alpha_pos = Positions[i, :].copy()
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if fitness > Alpha_score and fitness < Beta_score:
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Delta_score = Beta_score # Update delte
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Delta_pos = Beta_pos.copy()
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Beta_score = fitness # Update beta
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Beta_pos = Positions[i, :].copy()
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if fitness > Alpha_score and fitness > Beta_score and fitness < Delta_score:
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Delta_score = fitness # Update delta
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Delta_pos = Positions[i, :].copy()
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a = 2 - l * ((2) / Max_iter)
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# a decreases linearly fron 2 to 0
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# Update the Position of search agents including omegas
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for i in range(0, SearchAgents_no):
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for j in range(0, dim):
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r1 = random.random() # r1 is a random number in [0,1]
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r2 = random.random() # r2 is a random number in [0,1]
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A1 = 2 * a * r1 - a
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# Equation (3.3)
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C1 = 2 * r2
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# Equation (3.4)
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D_alpha = abs(C1 * Alpha_pos[j] - Positions[i, j])
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# Equation (3.5)-part 1
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X1 = Alpha_pos[j] - A1 * D_alpha
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# Equation (3.6)-part 1
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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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# Equation (3.3)
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C2 = 2 * r2
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# Equation (3.4)
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D_beta = abs(C2 * Beta_pos[j] - Positions[i, j])
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# Equation (3.5)-part 2
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X2 = Beta_pos[j] - A2 * D_beta
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# Equation (3.6)-part 2
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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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# Equation (3.3)
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C3 = 2 * r2
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# Equation (3.4)
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D_delta = abs(C3 * Delta_pos[j] - Positions[i, j])
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# Equation (3.5)-part 3
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X3 = Delta_pos[j] - A3 * D_delta
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# Equation (3.5)-part 3
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Positions[i, j] = (X1 + X2 + X3) / 3 # Equation (3.7)
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Convergence_curve[l] = Alpha_score
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if l % 1 == 0:
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print(["At iteration " + str(l) + " the best fitness is " + str(Alpha_score)])
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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 = "GWO"
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s.bestIndividual = Alpha_pos
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s.objfname = objf.__name__
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return s
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