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Merge pull request #52 from bionboy/simple_code_format
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Code Formatting
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@7ossam81
7ossam81 authored Nov 27, 2020
2 parents b683065 + 1e0fd64 commit d8c1edb
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535 changes: 364 additions & 171 deletions benchmarks.py
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24 changes: 14 additions & 10 deletions example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,22 +9,26 @@

# Select optimizers
# "SSA","PSO","GA","BAT","FFA","GWO","WOA","MVO","MFO","CS","HHO","SCA","JAYA","DE"
optimizer=["SSA","PSO","GWO"]
optimizer = ["SSA", "PSO", "GWO"]

# Select benchmark function"
# "F1","F2","F3","F4","F5","F6","F7","F8","F9","F10","F11","F12","F13","F14","F15","F16","F17","F18","F19"
# "Ca1","Ca2","Gt1","Mes","Mef","Sag","Tan","Ros"
objectivefunc=["F3","F4"]
# "Ca1","Ca2","Gt1","Mes","Mef","Sag","Tan","Ros"
objectivefunc = ["F3", "F4"]

# Select number of repetitions for each experiment.
# Select number of repetitions for each experiment.
# To obtain meaningful statistical results, usually 30 independent runs are executed for each algorithm.
NumOfRuns=3
NumOfRuns = 3

# Select general parameters for all optimizers (population size, number of iterations) ....
params = {'PopulationSize' : 30, 'Iterations' : 50}

#Choose whether to Export the results in different formats
export_flags = {'Export_avg':True, 'Export_details':True,
'Export_convergence':True, 'Export_boxplot':True}
params = {"PopulationSize": 30, "Iterations": 50}

# Choose whether to Export the results in different formats
export_flags = {
"Export_avg": True,
"Export_details": True,
"Export_convergence": True,
"Export_boxplot": True,
}

run(optimizer, objectivefunc, NumOfRuns, params, export_flags)
204 changes: 109 additions & 95 deletions optimizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,44 +28,45 @@
import plot_convergence as conv_plot
import plot_boxplot as box_plot

warnings.simplefilter(action='ignore')

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=="SSA"):
x=ssa.SSA(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter)
elif(algo=="PSO"):
x=pso.PSO(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter)
elif(algo=="GA"):
x=ga.GA(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter)
elif(algo=="BAT"):
x=bat.BAT(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter)
elif(algo=="FFA"):
x=ffa.FFA(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter)
elif(algo=="GWO"):
x=gwo.GWO(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter)
elif(algo=="WOA"):
x=woa.WOA(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter)
elif(algo=="MVO"):
x=mvo.MVO(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter)
elif(algo=="MFO"):
x=mfo.MFO(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter)
elif(algo=="CS"):
x=cs.CS(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter)
elif(algo=="HHO"):
x=hho.HHO(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter)
elif(algo=="SCA"):
x=sca.SCA(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter)
elif(algo=="JAYA"):
x=jaya.JAYA(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter)
elif(algo=="DE"):
x=de.DE(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter)
warnings.simplefilter(action="ignore")


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 == "SSA":
x = ssa.SSA(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter)
elif algo == "PSO":
x = pso.PSO(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter)
elif algo == "GA":
x = ga.GA(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter)
elif algo == "BAT":
x = bat.BAT(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter)
elif algo == "FFA":
x = ffa.FFA(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter)
elif algo == "GWO":
x = gwo.GWO(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter)
elif algo == "WOA":
x = woa.WOA(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter)
elif algo == "MVO":
x = mvo.MVO(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter)
elif algo == "MFO":
x = mfo.MFO(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter)
elif algo == "CS":
x = cs.CS(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter)
elif algo == "HHO":
x = hho.HHO(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter)
elif algo == "SCA":
x = sca.SCA(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter)
elif algo == "JAYA":
x = jaya.JAYA(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter)
elif algo == "DE":
x = de.DE(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter)
else:
return null;
return null
return x


Expand All @@ -75,100 +76,113 @@ def run(optimizer, objectivefunc, NumOfRuns, params, export_flags):
It serves as the main interface of the framework for running the experiments.
Parameters
----------
----------
optimizer : list
The list of optimizers names
objectivefunc : list
The list of benchmark functions
NumOfRuns : int
The number of independent runs
The number of independent runs
params : set
The set of parameters which are:
The set of parameters which are:
1. Size of population (PopulationSize)
2. The number of iterations (Iterations)
export_flags : set
The set of Boolean flags which are:
The set of Boolean flags which are:
1. Export (Exporting the results in a file)
2. Export_details (Exporting the detailed results in files)
3. Export_convergence (Exporting the covergence plots)
4. Export_boxplot (Exporting the box plots)
Returns
-----------
N/A
"""

# Select general parameters for all optimizers (population size, number of iterations) ....
PopulationSize = params['PopulationSize']
Iterations= params['Iterations']
PopulationSize = params["PopulationSize"]
Iterations = params["Iterations"]

#Export results ?
Export=export_flags['Export_avg']
Export_details=export_flags['Export_details']
Export_convergence = export_flags['Export_convergence']
Export_boxplot = export_flags['Export_boxplot']
# Export results ?
Export = export_flags["Export_avg"]
Export_details = export_flags["Export_details"]
Export_convergence = export_flags["Export_convergence"]
Export_boxplot = export_flags["Export_boxplot"]

Flag=False
Flag_details=False
Flag = False
Flag_details = False

# CSV Header for for the cinvergence
CnvgHeader=[]
# CSV Header for for the cinvergence
CnvgHeader = []

results_directory = time.strftime("%Y-%m-%d-%H-%M-%S") + '/'
results_directory = time.strftime("%Y-%m-%d-%H-%M-%S") + "/"
Path(results_directory).mkdir(parents=True, exist_ok=True)

for l in range(0,Iterations):
CnvgHeader.append("Iter"+str(l+1))
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(objectivefunc)):
convergence = [0]*NumOfRuns
executionTime = [0]*NumOfRuns
for k in range (0,NumOfRuns):
func_details=benchmarks.getFunctionDetails(objectivefunc[j])
x=selector(optimizer[i],func_details,PopulationSize,Iterations)
for i in range(0, len(optimizer)):
for j in range(0, len(objectivefunc)):
convergence = [0] * NumOfRuns
executionTime = [0] * NumOfRuns
for k in range(0, NumOfRuns):
func_details = benchmarks.getFunctionDetails(objectivefunc[j])
x = selector(optimizer[i], func_details, PopulationSize, Iterations)
convergence[k] = x.convergence
optimizerName = x.optimizer
objfname = x.objfname
if(Export_details==True):
ExportToFile=results_directory + "experiment_details.csv"
with open(ExportToFile, 'a',newline='\n') as out:
writer = csv.writer(out,delimiter=',')
if (Flag_details==False): # just one time to write the header of the CSV file
header= numpy.concatenate([["Optimizer","objfname","ExecutionTime"],CnvgHeader])
objfname = x.objfname
if Export_details == True:
ExportToFile = results_directory + "experiment_details.csv"
with open(ExportToFile, "a", newline="\n") as out:
writer = csv.writer(out, delimiter=",")
if (
Flag_details == False
): # just one time to write the header of the CSV file
header = numpy.concatenate(
[["Optimizer", "objfname", "ExecutionTime"], CnvgHeader]
)
writer.writerow(header)
Flag_details=True # at least one experiment
Flag_details = True # at least one experiment
executionTime[k] = x.executionTime
a=numpy.concatenate([[x.optimizer,x.objfname,x.executionTime],x.convergence])
a = numpy.concatenate(
[[x.optimizer, x.objfname, x.executionTime], x.convergence]
)
writer.writerow(a)
out.close()

if(Export==True):
ExportToFile=results_directory + "experiment.csv"

with open(ExportToFile, 'a',newline='\n') 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","ExecutionTime"],CnvgHeader])
writer.writerow(header)
Flag=True

avgExecutionTime = float("%0.2f"%(sum(executionTime) / NumOfRuns))
avgConvergence = numpy.around(numpy.mean(convergence, axis=0, dtype=numpy.float64), decimals=2).tolist()
a=numpy.concatenate([[optimizerName,objfname,avgExecutionTime],avgConvergence])
if Export == True:
ExportToFile = results_directory + "experiment.csv"

with open(ExportToFile, "a", newline="\n") 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", "ExecutionTime"], CnvgHeader]
)
writer.writerow(header)
Flag = True

avgExecutionTime = float("%0.2f" % (sum(executionTime) / NumOfRuns))
avgConvergence = numpy.around(
numpy.mean(convergence, axis=0, dtype=numpy.float64), decimals=2
).tolist()
a = numpy.concatenate(
[[optimizerName, objfname, avgExecutionTime], avgConvergence]
)
writer.writerow(a)
out.close()

if Export_convergence == True:
conv_plot.run(results_directory, optimizer, objectivefunc, Iterations)



if Export_boxplot == True:
box_plot.run(results_directory, optimizer, objectivefunc, Iterations)

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")

print("Execution completed")

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"
)

print("Execution completed")
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