from models import ArtificialBeeColony
from optproblems import cec2005
from os import makedirs
import statistics
import csv
if __name__ == '__main__':
dims = 10
bounds = [[-100 for i in range(dims)], [100 for i in range(dims)]]
functions = [
cec2005.F1(dims),
cec2005.F2(dims),
cec2005.F3(dims),
cec2005.F4(dims),
cec2005.F5(dims)
]
optimums = [-450, -450, -450, -450, -310]
FESThresholds = [
0, 0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1
]
numRuns = 25
# Creating result path
pathName = "Results/ABC"
makedirs(pathName, exist_ok=True)
# Initializing result files
tableFileName = pathName + "/ABC_" + str(dims) + "D.csv"
from models import AdaptiveGA
from optproblems import cec2005
from os import makedirs
import statistics
import csv
if __name__ == '__main__':
dims = 10
bounds = [ [-100 for i in range(dims)], [100 for i in range(dims)] ]
functions = [ cec2005.F1(dims), cec2005.F2(dims), cec2005.F3(dims), cec2005.F4(dims), cec2005.F5(dims)]
optimums = [-450, -450, -450, -450, -310]
FESThresholds = [0, 0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]
numRuns = 25
# Creating result path
pathName = "Results/AGA"
makedirs(pathName, exist_ok=True)
# Initializing result files
tableFileName = pathName + "/AGA_" + str(dims) + "D.csv"
with open(tableFileName, "w") as resultsFile:
writer = csv.writer(resultsFile, delimiter = ',')
writer.writerow( ["Fi_10D", "Best", "Worst", "Median", "Mean", "Std_Dev", "Success_Rate"] )
}
if __name__ == '__main__':
# Test of the PSO's performance over CEC2005's F1 (shifted sphere)
import time
from optproblems import cec2005
np.seterr("raise") # any calculation error immediately stops the execution
bounds = [[-100 for i in range(10)],
[100 for i in range(10)]] # 10-dimensional sphere (optimum: 0)
start = time.time()
# Initialization
SSO = SocialSpiderOptimization(cec2005.F2(10), bounds, popSize=30, PF=0.7)
SSO.execute()
results = SSO.results
print("SSO: for criterion = " + SSO.crit + ", reached optimum of " +
str(results["bestFits"][-1]) + " (error of " +
str(results["errors"][-1]) + ") (points " +
str(results["bestPoints"][-1]) + ") with " +
str(results["generations"][-1]) + " generations" + " and " +
str(results["FESCounts"][-1]) + " fitness evaluations")
end = time.time()
print("time:" + str(end - start))
