126 lines
3.5 KiB
Python
126 lines
3.5 KiB
Python
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 SSA(objf, lb, ub, dim, N, Max_iteration):
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# Max_iteration=1000
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# lb=-100
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# ub=100
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# dim=30
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N = 50 # Number of search agents
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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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Convergence_curve = numpy.zeros(Max_iteration)
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# Initialize the positions of salps
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SalpPositions = numpy.zeros((N, dim))
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for i in range(dim):
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SalpPositions[:, i] = numpy.random.uniform(0, 1, N) * (ub[i] - lb[i]) + lb[i]
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SalpFitness = numpy.full(N, float("inf"))
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FoodPosition = numpy.zeros(dim)
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FoodFitness = float("inf")
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# Moth_fitness=numpy.fell(float("inf"))
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s = solution()
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print('SSA 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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for i in range(0, N):
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# evaluate moths
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SalpFitness[i] = objf(SalpPositions[i, :])
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sorted_salps_fitness = numpy.sort(SalpFitness)
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I = numpy.argsort(SalpFitness)
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Sorted_salps = numpy.copy(SalpPositions[I, :])
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FoodPosition = numpy.copy(Sorted_salps[0, :])
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FoodFitness = sorted_salps_fitness[0]
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Iteration = 1
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# Main loop
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while Iteration < Max_iteration:
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# Number of flames Eq. (3.14) in the paper
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# Flame_no=round(N-Iteration*((N-1)/Max_iteration));
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c1 = 2 * math.exp(-((4 * Iteration / Max_iteration) ** 2))
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# Eq. (3.2) in the paper
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for i in range(0, N):
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SalpPositions = numpy.transpose(SalpPositions)
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if i < N / 2:
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for j in range(0, dim):
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c2 = random.random()
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c3 = random.random()
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# Eq. (3.1) in the paper
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if c3 < 0.5:
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SalpPositions[j, i] = FoodPosition[j] + c1 * (
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(ub[j] - lb[j]) * c2 + lb[j]
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)
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else:
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SalpPositions[j, i] = FoodPosition[j] - c1 * (
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(ub[j] - lb[j]) * c2 + lb[j]
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)
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####################
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elif i >= N / 2 and i < N + 1:
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point1 = SalpPositions[:, i - 1]
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point2 = SalpPositions[:, i]
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SalpPositions[:, i] = (point2 + point1) / 2
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# Eq. (3.4) in the paper
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SalpPositions = numpy.transpose(SalpPositions)
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for i in range(0, N):
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# Check if salps go out of the search spaceand bring it back
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for j in range(dim):
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SalpPositions[i, j] = numpy.clip(SalpPositions[i, j], lb[j], ub[j])
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SalpFitness[i] = objf(SalpPositions[i, :])
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if SalpFitness[i] < FoodFitness:
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FoodPosition = numpy.copy(SalpPositions[i, :])
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FoodFitness = SalpFitness[i]
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# Display best fitness along the iteration
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if Iteration % 1 == 0:
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print(
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[
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"At iteration "
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+ str(Iteration)
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+ " the best fitness is "
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+ str(FoodFitness)
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]
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)
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Convergence_curve[Iteration] = FoodFitness
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Iteration = Iteration + 1
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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 = "SSA"
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s.bestIndividual = FoodPosition
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s.objfname = objf.__name__
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return s
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