JAYA.zip

""" JAYA Algorithm """ import random import numpy import math from solution import solution import time def JAYA(objf, lb, ub, dim, SearchAgents_no, Max_iter): # Best and Worst position initialization Best_pos = numpy.zeros(dim) Best_score = float("inf") Worst_pos = numpy.zeros(dim) Worst_score = float(0) fitness_matrix = numpy.zeros((SearchAgents_no)) if not isinstance(lb, list): lb = [lb] * dim if not isinstance(ub, list): ub = [ub] * dim # Initialize the positions of search agents Positions = numpy.zeros((SearchAgents_no, dim)) for i in range(dim): Positions[:, i] = ( numpy.random.uniform(0, 1, SearchAgents_no) * (ub[i] - lb[i]) + lb[i] ) for i in range(0, SearchAgents_no): # Return back the search agents that go beyond the boundaries of the search space for j in range(dim): Positions[i, j] = numpy.clip(Positions[i, j], lb[j], ub[j]) # Calculate objective function for each search agent fitness = objf(Positions[i]) fitness_matrix[i] = fitness if fitness < Best_score: Best_score = fitness # Update Best_Score Best_pos = Positions[i] if fitness > Worst_score: Worst_score = fitness # Update Worst_Score Worst_pos = Positions[i] Convergence_curve = numpy.zeros(Max_iter) s = solution() # Loop counter print('JAYA is optimizing "' + objf.__name__ + '"') timerStart = time.time() s.startTime = time.strftime("%Y-%m-%d-%H-%M-%S") # Main loop for l in range(0, Max_iter): # Update the Position of search agents for i in range(0, SearchAgents_no): New_Position = numpy.zeros(dim) for j in range(0, dim): # Update r1, r2 r1 = random.random() r2 = random.random() # JAYA Equation New_Position[j] = ( Positions[i][j] + r1 * (Best_pos[j] - abs(Positions[i, j])) - r2 * (Worst_pos[j] - abs(Positions[i, j])) ) # checking if New_Position[j] lies in search space if New_Position[j] > ub[j]: New_Position[j] = ub[j] if New_Position[j] < lb[j]: New_Position[j] = lb[j] new_fitness = objf(New_Position) current_fit = fitness_matrix[i] # replacing current element with new element if it has better fitness if new_fitness < current_fit: Positions[i] = New_Position fitness_matrix[i] = new_fitness # finding the best and worst element for i in range(SearchAgents_no): if fitness_matrix[i] < Best_score: Best_score = fitness_matrix[i] Best_pos = Positions[i, :].copy() if fitness_matrix[i] > Worst_score: Worst_score = fitness_matrix[i] Worst_pos = Positions[i, :].copy() Convergence_curve[l] = Best_score if l % 1 == 0: print( ["At iteration " + str(l) + " the best fitness is " + str(Best_score)] ) timerEnd = time.time() s.endTime = time.strftime("%Y-%m-%d-%H-%M-%S") s.executionTime = timerEnd - timerStart s.convergence = Convergence_curve s.optimizer = "JAYA" s.objfname = objf.__name__ return s