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main_single_objective.py
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main_single_objective.py
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#!/usr/bin/env python
# ------------------------------------------------------------------------------------------------------%
# Created by "Thieu" at 16:41, 11/01/2021 %
# %
# Email: [email protected] %
# Homepage: https://www.researchgate.net/profile/Nguyen_Thieu2 %
# Github: https://github.com/thieu1995 %
# ------------------------------------------------------------------------------------------------------%
from sklearn.model_selection import ParameterGrid
import multiprocessing
from numpy import array, ones, zeros, concatenate
from time import time
from pathlib import Path
from copy import deepcopy
from config import Config, OptParas, OptExp
from utils.io_util import load_tasks, load_nodes
from utils.experiment_util import save_experiment_results_single, save_visualization_results_single
import optimizer
def inside_loop(my_model, n_trials, n_timebound, epoch, fe, end_paras):
for n_tasks in OptExp.N_TASKS:
tasks = load_tasks(f'{Config.INPUT_DATA}/tasks_{n_tasks}.json')
problem = deepcopy(my_model['problem'])
problem["tasks"] = tasks
problem["n_tasks"] = n_tasks
problem["shape"] = n_tasks
problem["lb"] = zeros(n_tasks)
problem["ub"] = (problem["n_clouds"] + problem["n_fogs"]) * ones(n_tasks)
for pop_size in OptExp.POP_SIZE:
parameters_grid = list(ParameterGrid(my_model["param_grid"]))
for paras in parameters_grid:
name_paras = f'{epoch}_{pop_size}_{end_paras}'
opt = getattr(optimizer, my_model["class"])(problem=problem, pop_size=pop_size, epoch=epoch,
func_eval=fe, lb=problem["lb"], ub=problem["ub"], verbose=OptExp.VERBOSE, paras=paras)
solution, best_fit, best_fit_list, time_total = opt.train()
if Config.TIME_BOUND_KEY:
path_results = f'{Config.RESULTS_DATA}/{n_timebound}s/task_{n_tasks}/{Config.METRICS}/{my_model["name"]}/{n_trials}'
else:
path_results = f'{Config.RESULTS_DATA}/no_time_bound/task_{n_tasks}/{Config.METRICS}/{my_model["name"]}/{n_trials}'
Path(path_results).mkdir(parents=True, exist_ok=True)
save_experiment_results_single(problem, solution, best_fit_list, name_paras, time_total, path_results, Config.SAVE_TRAINING_RESULTS)
if Config.VISUAL_SAVING:
save_visualization_results_single(problem, solution, best_fit, my_model["name"], name_paras, path_results)
def setting_and_running(my_model):
print(f'Start running: {my_model["name"]}')
for n_trials in range(OptExp.N_TRIALS):
if Config.TIME_BOUND_KEY:
for n_timebound in OptExp.TIME_BOUND_VALUES:
if Config.MODE == "epoch":
for epoch in OptExp.EPOCH:
end_paras = f"{epoch}"
inside_loop(my_model, n_trials, n_timebound, epoch, None, end_paras)
elif Config.MODE == "fe":
for fe in OptExp.FE:
end_paras = f"{fe}"
inside_loop(my_model, n_trials, n_timebound, None, fe, end_paras)
else:
if Config.MODE == "epoch":
for epoch in OptExp.EPOCH:
end_paras = f"{epoch}"
inside_loop(my_model, n_trials, None, epoch, None, end_paras)
elif Config.MODE == "fe":
for fe in OptExp.FE:
end_paras = f"{fe}"
inside_loop(my_model, n_trials, None, None, fe, end_paras)
if __name__ == '__main__':
starttime = time()
clouds, fogs, peers = load_nodes(f'{Config.INPUT_DATA}/nodes_4_10_7.json')
problem = {
"clouds": clouds,
"fogs": fogs,
"peers": peers,
"n_clouds": len(clouds),
"n_fogs": len(fogs),
"n_peers": len(peers),
}
models = [
#{"name": "GA", "class": "BaseGA", "param_grid": OptParas.GA, "problem": problem},
#{"name": "PSO", "class": "BasePSO", "param_grid": OptParas.PSO, "problem": problem},
#{"name": "C-PSO", "class": "CPSO", "param_grid": OptParas.PSO, "problem": problem},
#{"name": "WOA", "class": "BaseWOA", "param_grid": OptParas.WOA, "problem": problem},
#{"name": "EO", "class": "BaseEO", "param_grid": OptParas.EO, "problem": problem},
#{"name": "AEO", "class": "BaseAEO", "param_grid": OptParas.AEO, "problem": problem},
#{"name": "SHADE", "class": "SHADE", "param_grid": OptParas.SHADE, "problem": problem},
#{"name": "A-GA", "class": "BaseGA", "param_grid": OptParas.GA, "problem": problem},
#{"name": "C-PSO", "class": "CPSO", "param_grid": OptParas.PSO, "problem": problem},
#{"name": "HI-WOA", "class": "HI_WOA", "param_grid": OptParas.HI_WOA, "problem": problem},
#{"name": "RW-EO", "class": "BaseEO", "param_grid": OptParas.EO, "problem": problem},
#{"name": "I-AEO", "class": "I_AEO", "param_grid": OptParas.AEO, "problem": problem},
#{"name": "LCO", "class": "BaseLCO", "param_grid": OptParas.LCO, "problem": problem},
# {"name": "IBLA", "class": "IBLA", "param_grid": OptParas.IBLA, "problem": problem},
# {"name": "I-LCO", "class": "I_LCO", "param_grid": OptParas.LCO, "problem": problem},
# {"name": "ILCO-2", "class": "ILCO_2", "param_grid": OptParas.LCO, "problem": problem},
{"name": "VNS", "class": "VNS", "param_grid": OptParas.LCO, "problem": problem},
#{"name": "SSA", "class": "BaseSSA", "param_grid": OptParas.SSA, "problem": problem},
#{"name": "WOA", "class": "BaseWOA", "param_grid": OptParas.WOA, "problem": problem},
]
processes = []
for my_md in models:
p = multiprocessing.Process(target=setting_and_running, args=(my_md,))
processes.append(p)
p.start()
for process in processes:
process.join()
print('That took: {} seconds'.format(time() - starttime))