moead_framework.algorithm.abstract_moead.AbstractMoead

class moead_framework.algorithm.abstract_moead.AbstractMoead(problem, max_evaluation, number_of_weight_neighborhood, aggregation_function, weight_file, termination_criteria=None, genetic_operator=None, parent_selector=None, mating_pool_selector=None, sps_strategy=None, offspring_generator=None, number_of_weight=None, number_of_objective=None)

Bases: object

Abstract class to implement a new algorithm based on MOEA/D in the framework

https://moead-framework.github.io/framework/html/tuto.html#implement-your-own-algorithm

__init__(problem, max_evaluation, number_of_weight_neighborhood, aggregation_function, weight_file, termination_criteria=None, genetic_operator=None, parent_selector=None, mating_pool_selector=None, sps_strategy=None, offspring_generator=None, number_of_weight=None, number_of_objective=None)

Constructor of the algorithm.

Parameters

• problem – {Problem} problem to optimize

• max_evaluation – {integer} maximum number of evaluation

• number_of_weight_neighborhood – {integer} size of the neighborhood.

• aggregation_function – {AggregationFunction}

• weight_file – {string} path of the weight file. Each line represent a weight vector, each column represent a coordinate. An exemple is available here: https://github.com/moead-framework/data/blob/master/weights/SOBOL-2objs-10wei.ws

• termination_criteria – Optional – {TerminationCriteria} The default component is {MaxEvaluation}

• genetic_operator – Optional – {GeneticOperator} The default operator depends of the problem type (combinatorial / numerical)

• parent_selector – Optional – {ParentSelector} The default operator depends of the number of solution required by the genetic operator

• mating_pool_selector – Optional – {MatingPoolSelector} The default selector is {ClosestNeighborsSelector}

• sps_strategy – Optional – {SpsStrategy} The default strategy is {SpsAllSubproblems}

• offspring_generator – Optional – {OffspringGenerator} The default generator is {OffspringGeneratorGeneric}

• number_of_weight – Deprecated – {integer} number of weight vector used to decompose the problem. Deprecated, remove in the next major release.

• number_of_objective – Deprecated – {integer} number of objective in the problem. Deprecated, remove in the next major release.

Methods

__init__(problem, max_evaluation, …[, …])	Constructor of the algorithm.
generate_closest_weight_vectors()	Generate all neighborhood for each solution in the population
generate_offspring(population)	Generate a new offspring.
get_sub_problems_to_visit()	Select sub-problems to visit for the next generation.
init_z()	Initialize the reference point z
initial_population()	Initialize the population of solution
mating_pool_selection(sub_problem)	Select the set of solutions where future parents solutions will be selected according to the current sub-problem visited
optimize_sub_problem(sub_problem_index)	Execute the process to optimize the sub-problem currently iterated
repair(solution)	Repair the solution in parameter.
run([checkpoint])	Execute the algorithm.
update_current_sub_problem(sub_problem)	Update the attribute current_sub_problem
update_solutions(solution, …)	Update solutions of the population and of the external archive ep
update_z(solution)	Update the reference point z with coordinates of the solution in parameter if coordinates are better.

generate_closest_weight_vectors()

Generate all neighborhood for each solution in the population

Returns

{list<List<Integer>>} List of sub-problem (neighborhood) for each solution

generate_offspring(population)

Generate a new offspring. This function calls the component OffspringGenerator

Parameters

population – {list<OneDimensionSolution>} set of solutions (parents) used to generate the offspring

Returns

{OneDimensionSolution} offspring

get_sub_problems_to_visit()

Select sub-problems to visit for the next generation. This function calls the component SpsStrategy

Returns

{list} indexes of sub-problems

init_z()

Initialize the reference point z

Returns

initial_population()

Initialize the population of solution

Returns

{List<OneDimensionSolution>}

mating_pool_selection(sub_problem)

Select the set of solutions where future parents solutions will be selected according to the current sub-problem visited

Parameters

sub_problem – {integer} index of the sub-problem currently visited

Returns

{list} indexes of sub-problems

optimize_sub_problem(sub_problem_index)

Execute the process to optimize the sub-problem currently iterated

Parameters

sub_problem_index – {integer} index of the sub-problem iterated

Returns

repair(solution)

Repair the solution in parameter.

Parameters

solution – {OneDimensionSolution}

Returns

{OneDimensionSolution} the repaired solution

run(checkpoint=None)

Execute the algorithm.

Parameters

checkpoint – {function} The default value is None. The checkpoint can be used to save data during the process. The function required one parameter of type {AbstractMoead}

Returns

list<{OneDimensionSolution}> All non-dominated solutions found by the algorithm

Example:

>>> from moead_framework.algorithm.combinatorial import Moead
>>> from moead_framework.tool.result import save_population
>>> moead = Moead(...)
>>>
>>> def checkpoint(moead_algorithm: AbstractMoead):
>>>     if moead_algorithm.current_eval % 10 == 0 :
>>>     filename = "non_dominated_solutions-eval" + str(moead_algorithm.current_eval) + ".txt"
>>>     save_population(file_name=filename, population=moead_algorithm.ep)
>>>
>>> population = moead.run(checkpoint)

update_current_sub_problem(sub_problem)

Update the attribute current_sub_problem

Parameters

sub_problem – {integer} index of sub-problem

Returns

update_solutions(solution, aggregation_function, sub_problem)

Update solutions of the population and of the external archive ep

Parameters

• solution – {OneDimensionSolution} the candidate solution also called offspring

• aggregation_function – {AggregationFunction} Aggregation function used to compare solution in a multi-objective context

• sub_problem – {integer} index of the sub-problem currently visited

Returns

update_z(solution)

Update the reference point z with coordinates of the solution in parameter if coordinates are better.

Parameters

solution – OneDimensionSolution solution used to update the reference point

Returns