swamoptimization.py

# -*- coding: utf-8 -*-
"""swamoptimization.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1Q8JAky0_G9oXmjPnonD2nVgSqQqNwQNP
"""

import numpy as np
import random



def Fitness(x , y) :
    h = x + 2 * - y + 3
    k = 2 * x + y - 8
    z =  h**2 + k**2
    return z


def VelocityUpdate(Particle, Velocity, PersonalBest, GlobalBest, WMin = 0.5 , Maximum = 1.0, C = 0.1):
    ParticleSize  = len(Particle)
    NewVelocity   = np.array([0.0 for i in range(ParticleSize)])
    r1  = random.uniform(0 , Maximum)
    r2  = random.uniform(0 , Maximum)
    w   = random.uniform(WMin , Maximum)
    c1 = C
    c2 = C
    for i in range(ParticleSize):
        NewVelocity[i] = w * Velocity[i] + c1 * r1 * (PersonalBest[i] - Particle[i]) + c2 * r2* (GlobalBest[i] - Particle[i])
    return NewVelocity


def PositionUpdate(Particle , Velocity) :
   NewParticle = Particle + Velocity
   return NewParticle

def PSO(Population, Dimension, MinimumPosition, MaximumPosition, Generation, FitnessCriteria):
    Particles     = [[random.uniform(MinimumPosition, MaximumPosition) for j in range(Dimension)] for i in range(Population)]
    PBestPosition = Particles
    PBestFitness  = [Fitness(p[0] , p[1]) for p in Particles]
    GBestIndex    = np.argmin(PBestFitness)
    GBestPosition = PBestPosition[GBestIndex]
    Velocity      = [[0.0 for j in range(Dimension)] for i in range(Population)]
    for t in range(Generation):
        if np.average(PBestFitness) <= FitnessCriteria:
            break
        else:
            for n in range(Population):
                Velocity[n]     = VelocityUpdate(Particles[n] , Velocity[n] , PBestPosition[n], GBestPosition)
                Particles[n]    = PositionUpdate(Particles[n], Velocity[n])
        PBestFitness = [Fitness(p[0] , p[1]) for p in Particles]
        GBestIndex = np.argmin(PBestFitness)
        GBestPosition = PBestPosition[GBestIndex]
    print('\nGlobal Best Position\t\t\t: ', GBestPosition)
    print('Best Fitness Value\t\t\t: ', min(PBestFitness))
    print('Average Particle Best Fitness Value\t: ', np.average(PBestFitness))
    print('Number of Generation\t\t\t: ', t , "\n")


Population  = 100
Dimension   = 2
MinPosition = -100
MaxPostion  = 100
Generation  = 400
Criteria    = 10e-4

PSO(
   Population       = Population    ,
   Dimension        = Dimension     ,
   MinimumPosition  = MinPosition   ,
   MaximumPosition  = MaxPostion    ,
   Generation       = Generation    ,
   FitnessCriteria  = Criteria
)