moead_framework.problem.numerical.zdt.Zdt1

class moead_framework.problem.numerical.zdt.Zdt1(size)

Bases: moead_framework.problem.problem.Problem

Implementation of the Zdt1 problem. https://sop.tik.ee.ethz.ch/download/supplementary/testproblems/zdt1/index.php

5. Zitzler, K. Deb, and L. Thiele. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8(2):173-195, 2000

Example:

>>> from moead_framework.problem.numerical import Zdt1
>>>
>>> zdt = Zdt1(size=10)
>>>
>>> # Generate a new solution
>>> solution = zdt.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__(size)

Constructor of the problem

Parameters

size – {integer} number of variables in a solution

Methods

__init__(size)	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
validate_size(size)

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, decision_vector)

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()

Generate a random solution for the current problem

Returns

{OneDimensionSolution}