moead_framework.problem.combinatorial.rmnk.Rmnk¶

class moead_framework.problem.combinatorial.rmnk.Rmnk(instance_file=None, rho=None, m=None, n=None, k=None, links=None, tables=None)[source]¶

Bases: moead_framework.problem.problem.Problem

Implementation of the multiobjective NK landscapes with tunable objective correlation. The problem is compatible with files generated by the mocobench generator http://mocobench.sourceforge.net/index.php?n=Problem.RMNK

Example:

>>> from moead_framework.problem.combinatorial import Rmnk
>>>
>>> # The file is available here : https://github.com/moead-framework/data/blob/master/problem/RMNK/Instances/rmnk_0_2_100_1_0.dat
>>> # Others instances are available here : https://github.com/moead-framework/data/tree/master/problem/RMNK/Instances
>>> instance_file = "moead_framework/test/data/instances/rmnk_0_2_100_1_0.dat"
>>> rmnk = Rmnk(instance_file=instance_file)
>>>
>>> # Generate a new solution
>>> solution = rmnk.generate_random_solution()
>>>
>>> # Print all decision variables of the solution
>>> print(solution.decision_vector)
>>>
>>> # Print all objectives values of the solution
>>> print(solution.F)

__init__(instance_file=None, rho=None, m=None, n=None, k=None, links=None, tables=None)[source]¶

Constructor of the problem

Parameters: objective_number – {integer}

Methods

`__init__`([instance_file, rho, m, n, k, …])	Constructor of the problem
`evaluate`(x)	Evaluate the given solution for the current problem and store the outcome
`f`(function_id, decision_vector)	Evaluate the decision_vector for the objective function_id
`generate_random_solution`()	Generate a random solution for the current problem
`init_with_data`(rho, m, n, k, links, tables)
`init_with_instance_file`(instance_file)
`load_links`(file_content)	Load links from the instance file
`load_tables`(file_content)	Load tables from the instance file
`sigma`(function_id, solution_array, item)	Compute the sigma value

dtype¶: alias of builtins.float

evaluate(x: Union[moead_framework.solution.base.Solution, Sequence]) → moead_framework.solution.one_dimension_solution.OneDimensionSolution¶

Evaluate the given solution for the current problem and store the outcome

Parameters: x – A {Solution} containing all decision variables
Returns: OneDimensionSolution

f(function_id: int, decision_vector: numpy.ndarray)[source]¶

Evaluate the decision_vector for the objective function_id

Parameters

function_id – {integer} index of the objective
decision_vector – {OneDimensionSolution} solution to evaluate

Returns

{float} fitness value

generate_random_solution()[source]¶

Generate a random solution for the current problem

Returns: {OneDimensionSolution}

load_links(file_content)[source]¶

Load links from the instance file

Parameters: file_content – {list<float>} content of the instance file
Returns: {integer} the number of line

load_tables(file_content)[source]¶

Load tables from the instance file

Parameters: file_content – {list<float>} content of the instance file
Returns

sigma(function_id, solution_array, item)[source]¶

Compute the sigma value

Parameters

function_id – {integer} index of the objective function
solution_array – {list<integer>} representation of the solution
item – {integer} index of the variable

Returns