metaheuristic_optimizer

This function is responsible for the metaheuristic optimization process. It is a general function that calls the specific algorithm functions.

df_all_reps, df_resume_all_reps,\
            reports, status = metaheuristic_optimizer(algorithm_setup,
                                                        general_setup)

Input variables

Name	Description	Type
algorithm_setup	Metaheuristic optimization setup. See algorithms documentation for more details. Use the same setup dictionary as the desired optimization method here	Dictionary
general_setup	Optimization process setup	Dictionary
general_setup keys
'number of repetitions'	Number of repetitions	Integer
'type code'	Type of population. Options: 'real code' or 'combinatorial code'	String
'initial pop. seed'	Random seed. Use None in list for random seed	List
'algorithm'	Optimization algorithm. See the available metaheuristic algorithms	String

Output variables

df_all_reps	All data on the population	Dataframe
df_resume_all_reps	Best data on the population	Dataframe
reports	Report about the repetition process	String
status	Best repetition \(id\)	Integer

Metaheuristic algorithms.

Function	Name
'hill_climbing_01'	Hill Climbing algorithm 01
'simulated_annealing_01'	Simulated Annealing algorithm 01
'genetic_algorithm_01'	Genetic Algorithm 01

Example 1

Use the hill climbing optimization method to optimize the 2D sphere function. Use a total of 100 iterations to perform the optimization. Consider the limits \(\mathbf{x}_L = [-5.0, -5.0]\) and \(\mathbf{x}_U = [5.0, 5.0]\) for the problem design variables. Use \(cov = 20%\), Gaussian random generator, and random initial guess. Run this complete process 30 times. Consider a population of 10 agents. Use hill_climbing_01 algorithm.

"""Object Function: of_file.py"""  
def my_function(x, none_variable):
    x_0 = x[0]
    x_1 = x[1]
    of = x_0 ** 2 + x_1 ** 2
    return of

"""Run optimization: your_problem.py or your_problem.ipynb"""
# import libray
# pip install metapy-toolbox or pip install --upgrade metapy-toolbox
from metapy_toolbox import metaheuristic_optimizer
from of_file import my_function # External .py file with your objective function

# Algorithm settings
algorithm_setup = {   
                    'number of iterations': 100,
                    'number of population': 2,
                    'number of dimensions': 2,
                    'x pop lower limit': [-5, -5],
                    'x pop upper limit': [5, 5],
                    'none variable': None,
                    'objective function': my_obj_function,
                    'algorithm parameters': {
                                                'mutation': {
                                                                'cov (%)': 20,
                                                                'pdf': 'gaussian'
                                                            }
                                            },
                  }

# METApy settings
general_setup = {   
            'number of repetitions': 30,
            'type code': 'real code',
            'initial pop. seed': [None] * 30,
            'algorithm': 'hill_climbing_01',
        }

# Run algorithm
df_all_reps, df_resume_all_reps, reports, status = metaheuristic_optimizer(algorithm_setup, general_setup)

 Optimization results: 

 - Best repetition id:  28
 - Best of:             5.3042292250e-16
 - Design variables:    [2.100789251887419e-08, 9.438822723774955e-09]
 - Process time (s):    3.779469

Analysis

See the details repetition \(id = 0\). df_resume_all_reps contains history details the best particle in \(id = 0\) repetition.

print(df_resume_all_reps[0])

To see all population history in repetition \(id = 0\) use:

print(df_all_reps[0])

See best \(id\) in repetitions:

print(status)

See best repetition:

print(df_resume_all_reps[status])

See complete report about best repetition:

# Report details
arq = "report_example.txt"

# Writing report
with open(arq, "w") as file:
    file.write(report[status])