Zdt5 (#163)

* Mod. experiments

* Added ZDT5

jmetal/lab/experiment.py

@@ -50,6 +50,9 @@ def execute(self, output_path: str = ""):
             file_name = os.path.join(output_path, "TIME.{}".format(self.run_tag))
             with open(file_name, "w+") as of:
                 of.write(str(self.algorithm.total_computing_time))
+
+    def get_algorithm_data(self):
+        return self.algorithm.observable_data()
 
 
 class Experiment:
@@ -63,12 +66,15 @@ def __init__(self, output_dir: str, jobs: List[Job], m_workers: int = 6):
         self.jobs = jobs
         self.m_workers = m_workers
         self.output_dir = output_dir
+        self.job_data = []
 
     def run(self) -> None:
         with ProcessPoolExecutor(max_workers=self.m_workers) as executor:
+
             for job in self.jobs:
                 output_path = os.path.join(self.output_dir, job.algorithm_tag, job.problem_tag)
                 executor.submit(job.execute(output_path))
+                self.job_data.append(job.get_algorithm_data())
 
 
 def generate_summary_from_experiment(

jmetal/problem/__init__.py

@@ -1,8 +1,8 @@
 from .multiobjective.constrained import Srinivas, Tanaka
-from .multiobjective.dtlz import DTLZ1, DTLZ2
+from .multiobjective.dtlz import *
 from .multiobjective.lz09 import LZ09_F2
 from .multiobjective.unconstrained import Fonseca, Kursawe, Schaffer, Viennet2
-from .multiobjective.zdt import ZDT1, ZDT2, ZDT3, ZDT4, ZDT6
+from .multiobjective.zdt import ZDT1, ZDT2, ZDT3, ZDT4, ZDT5, ZDT6
 from .singleobjective.tsp import TSP
 from .singleobjective.unconstrained import OneMax, Sphere
 
@@ -15,10 +15,16 @@
     "Viennet2",
     "DTLZ1",
     "DTLZ2",
+    "DTLZ3",
+    "DTLZ4",
+    "DTLZ5",
+    "DTLZ6",
+    "DTLZ7",
     "ZDT1",
     "ZDT2",
     "ZDT3",
     "ZDT4",
+    "ZDT5",
     "ZDT6",
     "LZ09_F2",
     "OneMax",

jmetal/problem/multiobjective/dtlz.py

@@ -30,6 +30,9 @@ def __init__(self, number_of_variables: int = 7, number_of_objectives=3):
 
     def number_of_objectives(self) -> int:
         return len(self.obj_directions)
+
+    def number_of_variables(self) -> int:
+        return len(self.lower_bound)
 
     def number_of_constraints(self) -> int:
         return 0
@@ -102,24 +105,24 @@ def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
         super(DTLZ3, self).__init__(number_of_variables, number_of_objectives)
 
     def evaluate(self, solution: FloatSolution) -> FloatSolution:
-        k = self.number_of_variables - self.number_of_objectives + 1
+        k = self.number_of_variables() - self.number_of_objectives() + 1
 
         g = sum(
-            [(x - 0.5) ** 2 - cos(20.0 * pi * (x - 0.5)) for x in solution.variables[self.number_of_variables - k :]]
+            [(x - 0.5) ** 2 - cos(20.0 * pi * (x - 0.5)) for x in solution.variables[self.number_of_variables() - k :]]
         )
         g = 100.0 * (k + g)
 
-        f = [1.0 + g for _ in range(self.number_of_objectives)]
+        f = [1.0 + g for _ in range(self.number_of_objectives())]
 
-        for i in range(self.number_of_objectives):
-            for j in range(self.number_of_objectives - (i + 1)):
+        for i in range(self.number_of_objectives()):
+            for j in range(self.number_of_objectives() - (i + 1)):
                 f[i] *= cos(solution.variables[j] * 0.5 * pi)
 
             if i != 0:
-                aux = self.number_of_objectives - (i + 1)
+                aux = self.number_of_objectives() - (i + 1)
                 f[i] *= sin(solution.variables[aux] * 0.5 * pi)
 
-        solution.objectives = [f[x] for x in range(self.number_of_objectives)]
+        solution.objectives = [f[x] for x in range(self.number_of_objectives())]
 
         return solution
 
@@ -139,20 +142,20 @@ def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
 
     def evaluate(self, solution: FloatSolution) -> FloatSolution:
         alpha = 100.0
-        k = self.number_of_variables - self.number_of_objectives + 1
+        k = self.number_of_variables() - self.number_of_objectives() + 1
 
-        g = sum([(x - 0.5) ** 2 for x in solution.variables[self.number_of_variables - k :]])
-        f = [1.0 + g for _ in range(self.number_of_objectives)]
+        g = sum([(x - 0.5) ** 2 for x in solution.variables[self.number_of_variables() - k :]])
+        f = [1.0 + g for _ in range(self.number_of_objectives())]
 
-        for i in range(self.number_of_objectives):
-            for j in range(self.number_of_objectives - (i + 1)):
+        for i in range(self.number_of_objectives()):
+            for j in range(self.number_of_objectives() - (i + 1)):
                 f[i] *= cos(pow(solution.variables[j], alpha) * pi / 2.0)
 
             if i != 0:
-                aux = self.number_of_objectives - (i + 1)
+                aux = self.number_of_objectives() - (i + 1)
                 f[i] *= sin(pow(solution.variables[aux], alpha) * pi / 2.0)
 
-        solution.objectives = [f[x] for x in range(self.number_of_objectives)]
+        solution.objectives = [f[x] for x in range(self.number_of_objectives())]
 
         return solution
 
@@ -171,26 +174,26 @@ def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
         super(DTLZ5, self).__init__(number_of_variables, number_of_objectives)
 
     def evaluate(self, solution: FloatSolution) -> FloatSolution:
-        k = self.number_of_variables - self.number_of_objectives + 1
+        k = self.number_of_variables() - self.number_of_objectives() + 1
 
-        g = sum([(x - 0.5) ** 2 for x in solution.variables[self.number_of_variables - k :]])
+        g = sum([(x - 0.5) ** 2 for x in solution.variables[self.number_of_variables() - k :]])
         t = pi / (4.0 * (1.0 + g))
 
-        theta = [0.0] * (self.number_of_objectives - 1)
+        theta = [0.0] * (self.number_of_objectives() - 1)
         theta[0] = solution.variables[0] * pi / 2.0
-        theta[1:] = [t * (1.0 + 2.0 * g * solution.variables[i]) for i in range(1, self.number_of_objectives - 1)]
+        theta[1:] = [t * (1.0 + 2.0 * g * solution.variables[i]) for i in range(1, self.number_of_objectives() - 1)]
 
-        f = [1.0 + g for _ in range(self.number_of_objectives)]
+        f = [1.0 + g for _ in range(self.number_of_objectives())]
 
-        for i in range(self.number_of_objectives):
-            for j in range(self.number_of_objectives - (i + 1)):
+        for i in range(self.number_of_objectives()):
+            for j in range(self.number_of_objectives() - (i + 1)):
                 f[i] *= cos(theta[j])
 
             if i != 0:
-                aux = self.number_of_objectives - (i + 1)
+                aux = self.number_of_objectives() - (i + 1)
                 f[i] *= sin(theta[aux])
 
-        solution.objectives = [f[x] for x in range(self.number_of_objectives)]
+        solution.objectives = [f[x] for x in range(self.number_of_objectives())]
 
         return solution
 
@@ -209,26 +212,26 @@ def __init__(self, number_of_variables: int = 12, number_of_objectives=3):
         super(DTLZ6, self).__init__(number_of_variables, number_of_objectives)
 
     def evaluate(self, solution: FloatSolution) -> FloatSolution:
-        k = self.number_of_variables - self.number_of_objectives + 1
+        k = self.number_of_variables() - self.number_of_objectives() + 1
 
-        g = sum([pow(x, 0.1) for x in solution.variables[self.number_of_variables - k :]])
+        g = sum([pow(x, 0.1) for x in solution.variables[self.number_of_variables() - k :]])
         t = pi / (4.0 * (1.0 + g))
 
-        theta = [0.0] * (self.number_of_objectives - 1)
+        theta = [0.0] * (self.number_of_objectives() - 1)
         theta[0] = solution.variables[0] * pi / 2.0
-        theta[1:] = [t * (1.0 + 2.0 * g * solution.variables[i]) for i in range(1, self.number_of_objectives - 1)]
+        theta[1:] = [t * (1.0 + 2.0 * g * solution.variables[i]) for i in range(1, self.number_of_objectives() - 1)]
 
-        f = [1.0 + g for _ in range(self.number_of_objectives)]
+        f = [1.0 + g for _ in range(self.number_of_objectives())]
 
-        for i in range(self.number_of_objectives):
-            for j in range(self.number_of_objectives - (i + 1)):
+        for i in range(self.number_of_objectives()):
+            for j in range(self.number_of_objectives() - (i + 1)):
                 f[i] *= cos(theta[j])
 
             if i != 0:
-                aux = self.number_of_objectives - (i + 1)
+                aux = self.number_of_objectives() - (i + 1)
                 f[i] *= sin(theta[aux])
 
-        solution.objectives = [f[x] for x in range(self.number_of_objectives)]
+        solution.objectives = [f[x] for x in range(self.number_of_objectives())]
 
         return solution
 
@@ -247,17 +250,17 @@ def __init__(self, number_of_variables: int = 22, number_of_objectives=3):
         super(DTLZ7, self).__init__(number_of_variables, number_of_objectives)
 
     def evaluate(self, solution: FloatSolution) -> FloatSolution:
-        k = self.number_of_variables - self.number_of_objectives + 1
+        k = self.number_of_variables() - self.number_of_objectives() + 1
 
-        g = sum([x for x in solution.variables[self.number_of_variables - k :]])
+        g = sum([x for x in solution.variables[self.number_of_variables() - k :]])
         g = 1.0 + (9.0 * g) / k
 
         h = sum(
-            [(x / (1.0 + g)) * (1 + sin(3.0 * pi * x)) for x in solution.variables[: self.number_of_objectives - 1]]
+            [(x / (1.0 + g)) * (1 + sin(3.0 * pi * x)) for x in solution.variables[: self.number_of_objectives() - 1]]
         )
-        h = self.number_of_objectives - h
+        h = self.number_of_objectives() - h
 
-        solution.objectives[: self.number_of_objectives - 1] = solution.variables[: self.number_of_objectives - 1]
+        solution.objectives[: self.number_of_objectives() - 1] = solution.variables[: self.number_of_objectives() - 1]
         solution.objectives[-1] = (1.0 + g) * h
 
         return solution

jmetal/problem/multiobjective/zdt.py

@@ -31,6 +31,9 @@ def __init__(self, number_of_variables: int = 30):
 
     def number_of_objectives(self) -> int:
         return len(self.obj_directions)
+
+    def number_of_variables(self) -> int:
+        return len(self.lower_bound)
 
     def number_of_constraints(self) -> int:
         return 0
@@ -135,6 +138,39 @@ def name(self):
         return "ZDT4"
 
 
+class ZDT5(ZDT1):
+    """Problem ZDT5.
+
+    .. note:: Bi-objective unconstrained problem. The default number of variables is 30.
+    .. note:: Continuous problem having a non-convex Pareto front
+    """
+
+    def evaluate(self, solution: FloatSolution) -> FloatSolution:
+        g = self.eval_g(solution)
+        h = self.eval_h(solution.variables[0], g)
+
+        solution.objectives[0] = 1.0 + g
+        solution.objectives[1] = h * (1.0 - (solution.objectives[0] / h) ** 0.25)
+
+        return solution
+
+    def eval_g(self, solution: FloatSolution):
+        g = sum(solution.variables[1:]) / (solution.number_of_variables - 1)
+
+        g = 1.0 + 9.0 * g
+
+        return g
+
+    def eval_h(self, f: float, g: float) -> float:
+        h = 1.0 - (f / g) ** 0.5 - (f / g) * sin(10.0 * pi * f)
+
+        return h
+
+    def name(self):
+        return "ZDT5"
+
+
+
 class ZDT6(ZDT1):
     """Problem ZDT6.