jMetalPy v1.5.5 (#79)

* Add solution generator and evaluator for SA (#67)

* Add warm startup for SA using population_generator.

* Revert evaluator parametrization in SA.

* Working on implementing a IntegerFloatSolution class

* Update nsgaiii.py (#70)

removed obsolete import

* Fix conflict in file smpsorp_zdt4.py

* Add new implementation of quality indicators. All of them receive a numpy array as a parameter instead of a list of solutions.

* Refactor quality indicators. All of them receive as a parameter a numpy array instead of a list of solutions

* Add file ZDT1.pf in the test folder (to be use to test quality indicators)

* Feature/mixed solution (#73)

* Working on implementing a IntegerFloatSolution class

* Add unit test cases for class IntegerFloatProblem

* Add class NMMin

* Add class IntegerFloatSBXCrossover

* Add test cases for SBXCrossover

* Still working on implementing an approach for the IntegerFloatSolution class

* Add user defined exceptiones in file checking.py

* Working on the implementation of class CompositeSolution

* Workon on class CompositeSolution

* Class CompositeMutation implemented and tested

* Fix a bug in class Neighborhood

* Class CompositeCrossover implemented and tested

* Add class

* Add class

* Rename file

* Add problem ZDT1Modified

* Add examples with NSGA-II

* Add NSGA-II examples

* Optimize imports

* Minor changes

* Changes on attribute name

Co-authored-by: Yebisu <ajnebro@outlook.com>

* Minor changes

* Release v1.5.4

* Updating the MOEAD and variants examples

* Minor changes

* Add problem UF1

* Fix bug in ibea.

* Refactor crossover and mutation operators

* Refactor crossover and mutation operators

* New minor version 1.5.5

Co-authored-by: Yevhenii Semendiak <32543098+YevheniiSemendiak@users.noreply.github.com>
Co-authored-by: ajnebro <ajnebro@outlook.com>
Co-authored-by: Marvin Steijaert <msteijaert@gmail.com>
Co-authored-by: benhid <atbnhd@gmail.com>
Co-authored-by: Sizhe Yuen <sizhe1007@gmail.com>
Co-authored-by: Antonio J. Nebro <ajnebro@users.noreply.github.com>

README.md

@@ -100,7 +100,7 @@ plot_front.plot(front, label='NSGAII-ZDT1', filename='NSGAII-ZDT1', format='png'
 <img src=docs/source/_static/NSGAII-ZDT1.png width=450 alt="Pareto front approximation">
 
 ## Features
-The current release of jMetalPy (v1.5.4) contains the following components:
+The current release of jMetalPy (v1.5.5) contains the following components:
 
 * Algorithms: local search, genetic algorithm, evolution strategy, simulated annealing, random search, NSGA-II, NSGA-III, SMPSO, OMOPSO, MOEA/D, MOEA/D-DRA, MOEA/D-IEpsilon, GDE3, SPEA2, HYPE, IBEA. Preference articulation-based algorithms (G-NSGA-II, G-GDE3, G-SPEA2, SMPSO/RP); Dynamic versions of NSGA-II, SMPSO, and GDE3.
 * Parallel computing based on Apache Spark and Dask.
@@ -118,6 +118,7 @@ The current release of jMetalPy (v1.5.4) contains the following components:
 
 ## Changelog
 
+* [v1.5.5] Minor bug fixes.
 * [v1.5.4] Refactored quality indicators to accept numpy array as input parameter.
 * [v1.5.4] Added [CompositeSolution](https://github.com/jMetal/jMetalPy/blob/master/jmetal/core/solution.py#L111) class to support mixed combinatorial problems. [#69](https://github.com/jMetal/jMetalPy/issues/69)
 

examples/multiobjective/moead/moead_dtlz2.py

@@ -1,4 +1,5 @@
 from jmetal.algorithm.multiobjective.moead import MOEAD
+from jmetal.core.quality_indicator import HyperVolume, InvertedGenerationalDistance
 from jmetal.operator import PolynomialMutation, DifferentialEvolutionCrossover
 from jmetal.problem import DTLZ2
 from jmetal.util.aggregative_function import Tschebycheff
@@ -14,7 +15,7 @@
     algorithm = MOEAD(
         problem=problem,
         population_size=300,
-        crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5, K=0.5),
+        crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5),
         mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
         aggregative_function=Tschebycheff(dimension=problem.number_of_objectives),
         neighbor_size=20,
@@ -27,6 +28,9 @@
     algorithm.run()
     front = algorithm.get_result()
 
+    hypervolume = HyperVolume([1.0, 1.0, 1.0])
+    print("Hypervolume: " + str(hypervolume.compute([front[i].objectives for i in range(len(front))])))
+
     # Save results to file
     print_function_values_to_file(front, 'FUN.' + algorithm.label)
     print_variables_to_file(front, 'VAR.'+ algorithm.label)

examples/multiobjective/moead/moead_iepsilon_lircmop1.py

@@ -15,7 +15,7 @@
     algorithm = MOEADIEpsilon(
         problem=problem,
         population_size=300,
-        crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5, K=0.5),
+        crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5),
         mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
         aggregative_function=Tschebycheff(dimension=problem.number_of_objectives),
         neighbor_size=20,

examples/multiobjective/moead/moead_lz09.py

@@ -15,7 +15,7 @@
     algorithm = MOEAD(
         problem=problem,
         population_size=300,
-        crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5, K=0.5),
+        crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5),
         mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
         aggregative_function=Tschebycheff(dimension=problem.number_of_objectives),
         neighbor_size=20,

examples/multiobjective/moead/moeaddra_lz09.py

@@ -1,20 +1,22 @@
 from jmetal.algorithm.multiobjective.moead import MOEAD_DRA
 from jmetal.operator import PolynomialMutation, DifferentialEvolutionCrossover
 from jmetal.problem import LZ09_F2
+from jmetal.problem.multiobjective.uf import UF1
 from jmetal.util.aggregative_function import Tschebycheff
 from jmetal.util.solution import read_solutions, print_function_values_to_file, print_variables_to_file
 from jmetal.util.termination_criterion import StoppingByEvaluations
+from jmetal.core.quality_indicator import HyperVolume, InvertedGenerationalDistance
 
 if __name__ == '__main__':
-    problem = LZ09_F2()
-    problem.reference_front = read_solutions(filename='resources/reference_front/LZ09_F2.pf')
+    problem = UF1()
+    problem.reference_front = read_solutions(filename='resources/reference_front/UF1.pf')
 
-    max_evaluations = 150000
+    max_evaluations = 300000
 
     algorithm = MOEAD_DRA(
         problem=problem,
-        population_size=300,
-        crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5, K=0.5),
+        population_size=600,
+        crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5),
         mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
         aggregative_function=Tschebycheff(dimension=problem.number_of_objectives),
         neighbor_size=20,
@@ -27,10 +29,15 @@
     algorithm.run()
     front = algorithm.get_result()
 
+    hypervolume = HyperVolume([2.0, 2.0])
+    print("Hypervolume: " + str(hypervolume.compute([front[i].objectives for i in range(len(front))])))
+
     # Save results to file
     print_function_values_to_file(front, 'FUN.' + algorithm.label)
     print_variables_to_file(front, 'VAR.'+ algorithm.label)
 
     print(f'Algorithm: ${algorithm.get_name()}')
     print(f'Problem: ${problem.get_name()}')
     print(f'Computing time: ${algorithm.total_computing_time}')
+
+

jmetal/algorithm/multiobjective/ibea.py

@@ -72,7 +72,7 @@ def compute_fitness_values(self, population: List[S], kappa: float) -> List[S]:
             for j in range(len(population)):
                 if j != i:
                     population[i].attributes['fitness'] += -np.exp(
-                        -EpsilonIndicator([population[i]]).compute([population[j]]) / self.kappa)
+                        -EpsilonIndicator([population[i].objectives]).compute([population[j].objectives]) / self.kappa)
         return population
 
     def create_initial_solutions(self) -> List[S]:
@@ -92,7 +92,7 @@ def replacement(self, population: List[S], offspring_population: List[S]) -> Lis
 
             for i in range(join_population_size):
                 join_population[i].attributes['fitness'] += np.exp(
-                    - EpsilonIndicator([join_population[i]]).compute([join_population[index_worst]]) / self.kappa)
+                    - EpsilonIndicator([join_population[i].objectives]).compute([join_population[index_worst].objectives]) / self.kappa)
 
             join_population.pop(index_worst)
             join_population_size = join_population_size - 1

jmetal/algorithm/singleobjective/evolution_strategy.py

@@ -7,6 +7,7 @@
 from jmetal.util.evaluator import Evaluator, SequentialEvaluator
 from jmetal.util.generator import Generator, RandomGenerator
 from jmetal.util.termination_criterion import TerminationCriterion
+from jmetal.util.constraint_handling import overall_constraint_violation_degree
 
 S = TypeVar('S')
 R = TypeVar('R')
@@ -78,7 +79,7 @@ def replacement(self, population: List[S], offspring_population: List[S]) -> Lis
         else:
             population_pool.extend(offspring_population)
 
-        population_pool.sort(key=lambda s: s.objectives[0])
+        population_pool.sort(key=lambda s: (overall_constraint_violation_degree(s), s.objectives[0]))
 
         new_population = []
         for i in range(self.mu):

jmetal/core/quality_indicator.py

@@ -47,7 +47,7 @@ def get_short_name(self) -> str:
 
 
 class GenerationalDistance(QualityIndicator):
-    def __init__(self, reference_front: np.array=None):
+    def __init__(self, reference_front: np.array = None):
         """
         * Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective Evolutionary Algorithm Research: A History and Analysis.
           Technical Report TR-98-03, Dept. Elec. Comput. Eng., Air Force. Inst. Technol. (1998)
@@ -71,11 +71,11 @@ def get_name(self) -> str:
 
 
 class InvertedGenerationalDistance(QualityIndicator):
-    def __init__(self, reference_front: np.array):
+    def __init__(self, reference_front: np.array = None):
         super(InvertedGenerationalDistance, self).__init__(is_minimization=True)
         self.reference_front = reference_front
 
-    def compute(self, solutions: np.array = None):
+    def compute(self, solutions: np.array):
         if self.reference_front is None:
             raise Exception('Reference front is none')
 

jmetal/core/test/test_solution.py

@@ -81,7 +81,6 @@ def test_should_copy_work_properly(self) -> None:
         self.assertEqual(solution.attributes, new_solution.attributes)
 
 
-
 class IntegerSolutionTestCase(unittest.TestCase):
 
     def test_should_constructor_create_a_non_null_object(self) -> None:
@@ -119,6 +118,7 @@ def test_should_copy_work_properly(self) -> None:
         self.assertIs(solution.upper_bound, solution.upper_bound)
         self.assertEqual(solution.attributes, new_solution.attributes)
 
+
 class CompositeSolutionTestCase(unittest.TestCase):
     def test_should_constructor_create_a_valid_not_none_composite_solution_composed_of_a_double_solution(self):
         composite_solution = CompositeSolution([FloatSolution([1.0], [2.0], 2)])
@@ -134,7 +134,7 @@ def test_should_constructor_raise_an_exception_if_the_number_of_objectives_is_no
     def test_should_constructor_create_a_valid_soltion_composed_of_a_float_and_an_integer_solutions(self):
         number_of_objectives = 3
         number_of_constraints = 1
-        float_solution: FloatSolution = FloatSolution([1.0], [3.0], number_of_objectives,  number_of_constraints)
+        float_solution: FloatSolution = FloatSolution([1.0], [3.0], number_of_objectives, number_of_constraints)
         integer_solution: IntegerSolution = IntegerSolution([2], [4], number_of_objectives, number_of_constraints)
 
         solution: CompositeSolution = CompositeSolution([float_solution, integer_solution])
@@ -153,7 +153,7 @@ def test_should_constructor_create_a_valid_soltion_composed_of_a_float_and_an_in
     def test_should_copy_work_properly(self):
         number_of_objectives = 3
         number_of_constraints = 1
-        float_solution: FloatSolution = FloatSolution([1.0], [3.0], number_of_objectives,  number_of_constraints)
+        float_solution: FloatSolution = FloatSolution([1.0], [3.0], number_of_objectives, number_of_constraints)
         integer_solution: IntegerSolution = IntegerSolution([2], [4], number_of_objectives, number_of_constraints)
 
         solution: CompositeSolution = CompositeSolution([float_solution, integer_solution])

jmetal/operator/crossover.py

@@ -17,7 +17,6 @@
 
 
 class NullCrossover(Crossover[Solution, Solution]):
-
     def __init__(self):
         super(NullCrossover, self).__init__(probability=0.0)
 
@@ -38,7 +37,6 @@ def get_name(self):
 
 
 class PMXCrossover(Crossover[PermutationSolution, PermutationSolution]):
-
     def __init__(self, probability: float):
         super(PMXCrossover, self).__init__(probability=probability)
 
@@ -97,7 +95,6 @@ def get_name(self):
 
 
 class CXCrossover(Crossover[PermutationSolution, PermutationSolution]):
-
     def __init__(self, probability: float):
         super(CXCrossover, self).__init__(probability=probability)
 
@@ -148,8 +145,8 @@ def __init__(self, probability: float, distribution_index: float = 20.0):
             raise Exception("The distribution index is negative: " + str(distribution_index))
 
     def execute(self, parents: List[FloatSolution]) -> List[FloatSolution]:
-        Check.that(type(parents[0]) is FloatSolution, "Solution type invalid: " + str(type(parents[0])))
-        Check.that(type(parents[1]) is FloatSolution, "Solution type invalid")
+        Check.that(issubclass(type(parents[0]), FloatSolution), "Solution type invalid: " + str(type(parents[0])))
+        Check.that(issubclass(type(parents[1]), FloatSolution), "Solution type invalid")
         Check.that(len(parents) == 2, 'The number of parents is not two: {}'.format(len(parents)))
 
         offspring = [copy.deepcopy(parents[0]), copy.deepcopy(parents[1])]
@@ -229,8 +226,8 @@ def __init__(self, probability: float, distribution_index: float = 20.0):
         self.distribution_index = distribution_index
 
     def execute(self, parents: List[IntegerSolution]) -> List[IntegerSolution]:
-        Check.that(type(parents[0]) is IntegerSolution, "Solution type invalid")
-        Check.that(type(parents[1]) is IntegerSolution, "Solution type invalid")
+        Check.that(issubclass(type(parents[0]), IntegerSolution), "Solution type invalid")
+        Check.that(issubclass(type(parents[1]), IntegerSolution), "Solution type invalid")
         Check.that(len(parents) == 2, 'The number of parents is not two: {}'.format(len(parents)))
 
         offspring = [copy.deepcopy(parents[0]), copy.deepcopy(parents[1])]
@@ -369,7 +366,7 @@ class DifferentialEvolutionCrossover(Crossover[FloatSolution, FloatSolution]):
     external to the crossover operator.
     """
 
-    def __init__(self, CR: float, F: float, K: float):
+    def __init__(self, CR: float, F: float, K: float = 0.5):
         super(DifferentialEvolutionCrossover, self).__init__(probability=1.0)
         self.CR = CR
         self.F = F

jmetal/operator/mutation.py

@@ -53,7 +53,7 @@ def __init__(self, probability: float, distribution_index: float = 0.20):
         self.distribution_index = distribution_index
 
     def execute(self, solution: FloatSolution) -> FloatSolution:
-        Check.that(type(solution) is FloatSolution, "Solution type invalid")
+        Check.that(issubclass(type(solution), FloatSolution), "Solution type invalid")
         for i in range(solution.number_of_variables):
             rand = random.random()
 
@@ -98,7 +98,7 @@ def __init__(self, probability: float, distribution_index: float = 0.20):
         self.distribution_index = distribution_index
 
     def execute(self, solution: IntegerSolution) -> IntegerSolution:
-        Check.that(type(solution) is IntegerSolution, "Solution type invalid")
+        Check.that(issubclass(type(solution), IntegerSolution), "Solution type invalid")
 
         for i in range(solution.number_of_variables):
             if random.random() <= self.probability:

jmetal/operator/test/test_crossover.py

@@ -1,4 +1,5 @@
 import unittest
+from typing import List
 from unittest import mock
 
 from jmetal.core.operator import Crossover
@@ -294,6 +295,27 @@ def test_should_execute_return_the_parents_if_the_crossover_probability_is_zero(
         self.assertEqual(solution1.variables, offspring[0].variables)
         self.assertEqual(solution2.variables, offspring[1].variables)
 
+    def test_should_execute_work_with_a_solution_subclass_of_float_solution(self):
+        class NewFloatSolution(FloatSolution):
+            def __init__(self, lower_bound: List[float], upper_bound: List[float], number_of_objectives: int,
+                         number_of_constraints: int = 0):
+                super(NewFloatSolution, self).__init__(lower_bound, upper_bound, number_of_objectives,
+                                                       number_of_constraints)
+
+        solution1 = NewFloatSolution([1, 2], [2, 4], 2, 2)
+        solution2 = NewFloatSolution([1, 2], [2, 4], 2, 2)
+
+        solution1.variables = [1.5, 2.7]
+        solution2.variables = [1.7, 3.6]
+
+        crossover: SBXCrossover = SBXCrossover(0.0, 20.0)
+        offspring = crossover.execute([solution1, solution2])
+
+        self.assertEqual(2, len(offspring))
+        self.assertEqual(solution1.variables, offspring[0].variables)
+        self.assertEqual(solution2.variables, offspring[1].variables)
+
+
     def test_should_execute_produce_valid_solutions_when_crossing_two_single_variable_solutions(self):
         pass
 
@@ -308,7 +330,7 @@ def test_should_constructor_raise_an_exception_if_the_parameter_list_is_Empty(se
             CompositeCrossover([])
 
     def test_should_constructor_create_a_valid_operator_when_adding_a_single_crossover_operator(self):
-        crossover: Crossover =  SBXCrossover(0.9, 20.0)
+        crossover: Crossover = SBXCrossover(0.9, 20.0)
 
         operator = CompositeCrossover([crossover])
         self.assertIsNotNone(operator)

jmetal/operator/test/test_mutation.py

@@ -1,4 +1,5 @@
 import unittest
+from typing import List
 
 from jmetal.core.operator import Mutation
 from jmetal.core.solution import BinarySolution, FloatSolution, IntegerSolution, CompositeSolution
@@ -46,8 +47,21 @@ def test_should_the_solution_change__if_the_probability_is_one(self):
         operator = PolynomialMutation(1.0)
         solution = FloatSolution([-5, -5, -5], [5, 5, 5], 2)
         solution.variables = [1.0, 2.0, 3.0]
-        FloatSolution.lower_bound = [-5, -5, -5]
-        FloatSolution.upper_bound = [5, 5, 5]
+
+        mutated_solution = operator.execute(solution)
+
+        self.assertNotEqual([1.0, 2.0, 3.0], mutated_solution.variables)
+
+    def test_should_execute_work_with_a_solution_subclass_of_float_solution(self):
+        class NewFloatSolution(FloatSolution):
+            def __init__(self, lower_bound: List[float], upper_bound: List[float], number_of_objectives: int,
+                         number_of_constraints: int = 0):
+                super(NewFloatSolution, self).__init__(lower_bound, upper_bound, number_of_objectives,
+                                                       number_of_constraints)
+
+        operator = PolynomialMutation(1.0)
+        solution = NewFloatSolution([-5, -5, -5], [5, 5, 5], 2)
+        solution.variables = [1.0, 2.0, 3.0]
 
         mutated_solution = operator.execute(solution)