119 lines
3.5 KiB
Python
119 lines
3.5 KiB
Python
""" JAYA 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 JAYA(objf, lb, ub, dim, SearchAgents_no, Max_iter):
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# Best and Worst position initialization
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Best_pos = numpy.zeros(dim)
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Best_score = float("inf")
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Worst_pos = numpy.zeros(dim)
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Worst_score = float(0)
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fitness_matrix = numpy.zeros((SearchAgents_no))
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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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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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fitness_matrix[i] = fitness
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if fitness < Best_score:
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Best_score = fitness # Update Best_Score
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Best_pos = Positions[i]
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if fitness > Worst_score:
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Worst_score = fitness # Update Worst_Score
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Worst_pos = Positions[i]
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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('JAYA 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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# Update the Position of search agents
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for i in range(0, SearchAgents_no):
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New_Position = numpy.zeros(dim)
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for j in range(0, dim):
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# Update r1, r2
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r1 = random.random()
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r2 = random.random()
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# JAYA Equation
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New_Position[j] = (
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Positions[i][j]
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+ r1 * (Best_pos[j] - abs(Positions[i, j]))
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- r2 * (Worst_pos[j] - abs(Positions[i, j]))
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)
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# checking if New_Position[j] lies in search space
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if New_Position[j] > ub[j]:
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New_Position[j] = ub[j]
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if New_Position[j] < lb[j]:
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New_Position[j] = lb[j]
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new_fitness = objf(New_Position)
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current_fit = fitness_matrix[i]
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# replacing current element with new element if it has better fitness
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if new_fitness < current_fit:
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Positions[i] = New_Position
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fitness_matrix[i] = new_fitness
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# finding the best and worst element
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for i in range(SearchAgents_no):
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if fitness_matrix[i] < Best_score:
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Best_score = fitness_matrix[i]
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Best_pos = Positions[i, :].copy()
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if fitness_matrix[i] > Worst_score:
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Worst_score = fitness_matrix[i]
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Worst_pos = Positions[i, :].copy()
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Convergence_curve[l] = Best_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(Best_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 = "JAYA"
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s.bestIndividual = Best_pos
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s.objfname = objf.__name__
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return s
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