Biogeography-Based-Optimization/optimizer.py at master · himanshuRepo/Biogeography-Based-Optimization · GitHub

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# -*- coding: utf-8 -*-
"""
Python code of Biogeography-Based Optimization (BBO)
Coded by: Raju Pal (emailid: raju3131.pal@gmail.com) and Himanshu Mittal (emailid: himanshu.mittal224@gmail.com)
The code template used is similar to code given at link: https://github.com/himanshuRepo/CKGSA-in-Python
 and matlab version of the BBO at link: http://embeddedlab.csuohio.edu/BBO/software/

Reference: D. Simon, Biogeography-Based Optimization, IEEE Transactions on Evolutionary Computation, in print (2008).
@author: Dan Simon (http://embeddedlab.csuohio.edu/BBO/software/)

-- Main File: Calling the Biogeography-Based Optimization(BBO) Algorithm
                for minimizing of an objective Function

Code compatible:
 -- Python: 2.* or 3.*
"""

import BBO as bbo
import benchmarks
import csv
import numpy
import time


def selector(algo,func_details,popSize,Iter):
    function_name=func_details[0]
    lb=func_details[1]
    ub=func_details[2]
    dim=func_details[3]

    if(algo==0):
        x=bbo.BBO(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter)
    return x


# Select optimizers
BBO= True # Code by Raju Pal & Himanshu Mittal


# Select benchmark function
F1=True
F2=False
F3=False
F4=False
F5=False
F6=False
F7=False
F8=False
F9=False
F10=False
F11=False
F12=False
F13=False
F14=False
F15=False
F16=False
F17=False
F18=False
F19=False


optimizer=[BBO]
benchmarkfunc=[F1,F2,F3,F4,F5,F6,F7,F8,F9,F10,F11,F12,F13,F14,F15,F16,F17,F18,F19]

# Select number of repetitions for each experiment.
# To obtain meaningful statistical results, usually 30 independent runs are executed for each algorithm.
NumOfRuns=2

# Select general parameters for all optimizers (population size, number of iterations)
PopulationSize = 30
Iterations= 500

#Export results ?
Export=True

#Automaticly generated name by date and time
ExportToFile="experiment"+time.strftime("%Y-%m-%d-%H-%M-%S")+".csv"

# Check if it works at least once
Flag=False

# CSV Header for for the cinvergence
CnvgHeader=[]

for l in range(0,Iterations):
	CnvgHeader.append("Iter"+str(l+1))


for i in range (0, len(optimizer)):
    for j in range (0, len(benchmarkfunc)):
        if((optimizer[i]==True) and (benchmarkfunc[j]==True)): # start experiment if an optimizer and an objective function is selected
            for k in range (0,NumOfRuns):

                func_details=benchmarks.getFunctionDetails(j)
                x=selector(i,func_details,PopulationSize,Iterations)
                if(Export==True):
                    with open(ExportToFile, 'a') as out:
                        writer = csv.writer(out,delimiter=',')
                        if (Flag==False): # just one time to write the header of the CSV file
                            header= numpy.concatenate([["Optimizer","objfname","startTime","EndTime","ExecutionTime"],CnvgHeader])
                            writer.writerow(header)
                        a=numpy.concatenate([[x.optimizer,x.objfname,x.startTime,x.endTime,x.executionTime],x.convergence])
                        writer.writerow(a)
                    out.close()
                Flag=True # at least one experiment

if (Flag==False): # Faild to run at least one experiment
    print("No Optomizer or Cost function is selected. Check lists of available optimizers and cost functions")