80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
# -*- coding: utf-8 -*-
|
|
"""
|
|
Python code of Spider-Monkey Optimization (SMO)
|
|
Coded by: Mukesh Saraswat (emailid: saraswatmukesh@gmail.com), Himanshu Mittal (emailid: himanshu.mittal224@gmail.com) and Raju Pal (emailid: raju3131.pal@gmail.com)
|
|
The code template used is similar to code given at link: https://github.com/himanshuRepo/CKGSA-in-Python
|
|
and C++ version of the SMO at link: http://smo.scrs.in/
|
|
|
|
Reference: Jagdish Chand Bansal, Harish Sharma, Shimpi Singh Jadon, and Maurice Clerc. "Spider monkey optimization algorithm for numerical optimization." Memetic computing 6, no. 1, 31-47, 2014.
|
|
@link: http://smo.scrs.in/
|
|
|
|
-- Main.py: Calling the Spider-Monkey Optimization (SMO) Algorithm
|
|
for minimizing of an objective Function
|
|
|
|
Code compatible:
|
|
-- Python: 2.* or 3.*
|
|
"""
|
|
import smo
|
|
import benchmarks
|
|
import csv
|
|
import numpy
|
|
import time
|
|
import math
|
|
|
|
|
|
def selector(func_details, popSize, Iter, succ_rate, mean_feval):
|
|
function_name = func_details[0]
|
|
lb = func_details[1]
|
|
ub = func_details[2]
|
|
dim = func_details[3]
|
|
acc_err = func_details[4]
|
|
obj_val = func_details[5]
|
|
|
|
|
|
x, succ_rate, mean_feval = smo.main(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter, acc_err,
|
|
obj_val, succ_rate, mean_feval)
|
|
return x, succ_rate, mean_feval
|
|
|
|
|
|
# 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 = 10
|
|
Iterations = 500
|
|
|
|
mean_error = 0
|
|
total_feval = 0
|
|
mean1 = 0
|
|
var = 0
|
|
sd = 0
|
|
mean_feval = 0
|
|
succ_rate = 0
|
|
GlobalMins = numpy.zeros(NumOfRuns)
|
|
|
|
for k in range(0, NumOfRuns):
|
|
|
|
func_details = benchmarks.getFunctionDetails()
|
|
print("Run: {}".format(k + 1))
|
|
x, succ_rate, mean_feval = selector(func_details, PopulationSize, Iterations, succ_rate, mean_feval)
|
|
mean_error = mean_error + x.error;
|
|
mean1 = mean1 + x.convergence[-1]
|
|
total_feval = total_feval + x.feval
|
|
GlobalMins[k] = x.convergence[-1]
|
|
|
|
|
|
mean1 = mean1 / NumOfRuns;
|
|
mean_error = mean_error / NumOfRuns
|
|
if (succ_rate > 0):
|
|
mean_feval = mean_feval / succ_rate
|
|
total_feval = total_feval / NumOfRuns
|
|
for k in range(NumOfRuns):
|
|
var = var + math.pow((GlobalMins[k] - mean1), 2)
|
|
var = var / NumOfRuns
|
|
sd = math.sqrt(var)
|
|
|
|
print(
|
|
"Values after executing SMO: \n Mean Error:{} \n Mean Function eval:{} \n Total Function eval:{} \n Variance:{} \n STD:{}".format(
|
|
mean_error, mean_feval, total_feval, var, sd))
|