94 lines
2.7 KiB
Python
94 lines
2.7 KiB
Python
""" Sine Cosine OPtimization Algorithm """
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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 SCA(objf, lb, ub, dim, SearchAgents_no, Max_iter):
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# destination_pos
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Dest_pos = numpy.zeros(dim)
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Dest_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('SCA 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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if fitness < Dest_score:
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Dest_score = fitness # Update Dest_Score
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Dest_pos = Positions[i, :].copy()
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# Eq. (3.4)
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a = 2
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Max_iteration = Max_iter
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r1 = a - l * ((a) / Max_iteration) # r1 decreases linearly from a to 0
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# Update the Position of search agents
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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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# Update r2, r3, and r4 for Eq. (3.3)
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r2 = (2 * numpy.pi) * random.random()
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r3 = 2 * random.random()
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r4 = random.random()
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# Eq. (3.3)
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if r4 < (0.5):
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# Eq. (3.1)
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Positions[i, j] = Positions[i, j] + (
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r1 * numpy.sin(r2) * abs(r3 * Dest_pos[j] - Positions[i, j])
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)
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else:
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# Eq. (3.2)
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Positions[i, j] = Positions[i, j] + (
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r1 * numpy.cos(r2) * abs(r3 * Dest_pos[j] - Positions[i, j])
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)
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Convergence_curve[l] = Dest_score
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if l % 1 == 0:
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print(
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["At iteration " + str(l) + " the best fitness is " + str(Dest_score)]
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)
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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 = "SCA"
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s.bestIndividual = Dest_pos
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s.objfname = objf.__name__
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return s
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