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run_multiple_algo2.py
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run_multiple_algo2.py
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#!/usr/bin/env python
# ------------------------------------------------------------------------------------------------------%
# Created by "Bao Hoang" at 15:43, 01/02/2020 %
# %
# Email: hoangnghiabao96@gmail.com %
# Homepage: https://www.researchgate.net/profile/Bao_Hoang19 %
# Github: https://github.com/hoangbao123 %
#-------------------------------------------------------------------------------------------------------%
import pickle as pkl
from models.evolution.GA import BaseGA
from utils.FunctionUtil import *
"""
GA
PSO
ABC - Artificial bee colony algorithm 2005
ABFOL - Adaptive Bacterial Foraging Optimization - 2012
CRO - The coral reefs optimization algorithm - 2014
CSO - Crisscross optimization algorithm - 2014
TWO - tug of war - 2016
WOA - 2016
QS0 - 2016
IQSO
"""
run_times = 15
problem_size = 30
epoch = 500
pop_size = 100
algo_dicts = {'GA': BaseGA}
# {'ABFOLS': ABFOLS, 'CRO': BaseCRO, 'ABC': BaseABC}
# GA': BaseGA}#,
# 'ABFOLS': ABFOLS, 'CRO': BaseCRO, 'TWO': BaseTWO,
fun_list = [C1, C2, C3, C4, C5, C6, C7, C8,
C9, C10, C11, C12, C13, C14, C15,
C16, C17, C18, C19, C20, C21, C22, C23,
C24, C25, C26, C27, C28, C29, C30]
global_min = [100, 200, 300, 400, 500, 600, 700,
800, 900, 1000, 1100, 1200, 1300,
1400, 1500, 1600, 1700, 1800, 1900,
2000, 2100, 2200, 2300, 2400, 2500,
2600, 2700, 2800, 2900, 3000]
# run each algo 15 time with 30 different benmark functions
res = {}
overall_name = ""
for name, Algo in algo_dicts.items():
std_list = []
mean_list = []
worst_list = []
best_list = []
print("-----------------------------------")
for i in range(23, len(fun_list)):
list_best_fit = []
gbest_fit = np.inf
gworst_fit = np.inf
std = 0
mean = 0
best_loss = []
for time in range(run_times):
print("name {}, fun {}, time {}/{}".format(name, i, time, run_times))
root_paras = {
"problem_size": problem_size,
"domain_range": [-100, 100],
"print_train": False,
"objective_func": fun_list[i]
}
if name == 'GA':
algo_paras = {
"epoch": epoch,
"pop_size": pop_size,
"pc": 0.95,
"pm": 0.025
}
md = Algo(root_paras, algo_paras)
elif name == 'PSO':
algo_paras = {
"epoch": epoch,
"pop_size": pop_size,
"w_minmax": [0.4, 0.9], # [0-1] -> [0.4-0.9] Weight of bird
"c_minmax": [1.2, 1.2] # [(1.2, 1.2), (0.8, 2.0), (1.6, 0.6)] Effecting of local va global
}
md = Algo(root_paras, algo_paras)
elif name == 'ABC':
algo_paras = {
"epoch": epoch,
"pop_size": pop_size,
"couple_bees": [16, 4], # number of bees which provided for good location and other location
"patch_variables": [5.0, 0.985], # patch_variables = patch_variables * patch_factor (0.985)
"sites": [3, 1], # 3 bees (employed bees, onlookers and scouts), 1 good partition
}
md = Algo(root_paras, algo_paras)
elif name == 'ABFOLS':
algo_paras = {
"epoch": epoch,
"pop_size": pop_size,
"Ci": [0.1, 0.001], # C_s (start), C_e (end) -=> step size # step size in BFO
"Ped": 0.25, # p_eliminate
"Ns": 4, # swim_length
"N_minmax": [3, 40], # (Dead threshold value, split threshold value) -> N_adapt, N_split
}
md = Algo(root_paras, algo_paras)
elif name == 'CRO':
algo_paras = {
"epoch": epoch,
"pop_size": pop_size,
"G": [0.02, 0.2],
"GCR": 0.1,
"po": 0.4,
"Fb": 0.9,
"Fa": 0.1,
"Fd": 0.1,
"Pd": 0.1,
"k": 3
}
md = Algo(root_paras, algo_paras)
elif name == 'TWO':
algo_paras = {
"epoch": epoch,
"pop_size": pop_size,
}
md = Algo(root_paras, algo_paras)
elif name == 'WOA':
algo_paras = {
"epoch": epoch,
"pop_size": pop_size
}
md = Algo(root_paras, algo_paras)
elif name == 'QSO':
algo_paras = {
"epoch": epoch,
"pop_size": pop_size,
}
md = Algo(root_paras, algo_paras)
elif name == 'IQSO':
algo_paras = {
"epoch": epoch,
"pop_size": pop_size,
}
md = Algo(root_paras, algo_paras)
_, loss_history, best_fit = md._train__()
if best_fit < gbest_fit:
gbest_fit = best_fit
best_loss = loss_history
list_best_fit.append(best_fit)
std = cal_std(list_best_fit, global_min[i])
mean = cal_mean(list_best_fit, global_min[i])
best = min(list_best_fit)
worst = max(list_best_fit)
std_list.append(std)
worst_list.append(worst)
best_list.append(best)
mean_list.append(mean)
fname = name + "_F" + str(i+1)
file_loss = fname + "_loss"
file_best_fit = fname + "_best_fit"
path_file_loss = './history/loss/' + file_loss
path_file_best_fit = './history/best_fit/' + file_best_fit
with open(path_file_loss + ".csv", 'w') as f_loss:
for loss in best_loss:
f_loss.write(str(loss) + '\n')
with open(path_file_loss + ".pkl", 'wb') as fo_loss:
pkl.dump(best_loss, fo_loss, pkl.HIGHEST_PROTOCOL)
with open(path_file_best_fit + ".csv", 'w') as f_fit:
for fit in list_best_fit:
f_fit.write(str(fit) + '\n')
with open(path_file_best_fit + ".pkl", 'wb') as fo_fit:
pkl.dump(list_best_fit, fo_fit, pkl.HIGHEST_PROTOCOL)
overall_name += name + "_"
overall_path = './history/overall/' + overall_name
res[name] = {'std': std_list, 'mean': mean_list, 'worst': worst_list, 'best': best_list}
with open(overall_path + '.csv', 'w') as f:
for k, v in res.items():
f.write(k + ',' + str(v) + '\n')
with open(overall_path + '.pkl', 'wb') as f:
pkl.dump(res, f, pkl.HIGHEST_PROTOCOL)