EvoloPy/SCA.py at master · MgeeeeK/EvoloPy · GitHub

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""" Sine Cosine OPtimization Algorithm """

import random
import numpy
import math
from solution import solution
import time


def SCA(objf,lb,ub,dim,SearchAgents_no,Max_iter):


    # destination_pos
    Dest_pos=numpy.zeros(dim)
    Dest_score=float("inf")

    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]

    Convergence_curve=numpy.zeros(Max_iter)
    s=solution()

     # Loop counter
    print("SCA 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):
        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,:])

            if fitness<Dest_score :
                Dest_score=fitness # Update Dest_Score
                Dest_pos=Positions[i,:].copy()


        # Eq. (3.4)
        a = 2
        Max_iteration = Max_iter
        r1=a-l*((a)/Max_iteration) # r1 decreases linearly from a to 0

        # Update the Position of search agents
        for i in range(0,SearchAgents_no):
            for j in range (0,dim):

                # Update r2, r3, and r4 for Eq. (3.3)
                r2=(2*numpy.pi)*random.random()
                r3=2*random.random()
                r4=random.random()

                # Eq. (3.3)
                if (r4< (0.5)):
                    # Eq. (3.1)
                    Positions[i,j]= Positions[i,j]+(r1*numpy.sin(r2)*abs(r3*Dest_pos[j]-Positions[i,j]))
                else:
                    # Eq. (3.2)
                    Positions[i,j]= Positions[i,j]+(r1*numpy.cos(r2)*abs(r3*Dest_pos[j]-Positions[i,j]))


        Convergence_curve[l]=Dest_score

        if (l%1==0):
               print(['At iteration '+ str(l)+ ' the best fitness is '+ str(Dest_score)])

    timerEnd=time.time()
    s.endTime=time.strftime("%Y-%m-%d-%H-%M-%S")
    s.executionTime=timerEnd-timerStart
    s.convergence=Convergence_curve
    s.optimizer="SCA"
    s.objfname=objf.__name__


    return s