Fix duplicate cost function calls in optimize() implementations (#266)

Resolves #257

pyswarms/backend/topology/ring.py

@@ -64,9 +64,7 @@ def compute_gbest(self, swarm, p, k, **kwargs):
             if (self.static and self.neighbor_idx is None) or not self.static:
                 # Obtain the nearest-neighbors for each particle
                 tree = cKDTree(swarm.position)
-                _, self.neighbor_idx = tree.query(
-                    swarm.position, p=p, k=k
-                )
+                _, self.neighbor_idx = tree.query(swarm.position, p=p, k=k)
 
             # Map the computed costs to the neighbour indices and take the
             # argmin. If k-neighbors is equal to 1, then the swarm acts

pyswarms/discrete/binary.py

@@ -158,16 +158,14 @@ def optimize(self, objective_func, iters, fast=False, **kwargs):
             lvl=logging.INFO,
         )
 
+        self.swarm.pbest_cost = np.full(self.swarm_size[0], np.inf)
         for i in self.rep.pbar(iters, self.name):
             if not fast:
                 sleep(0.01)
             # Compute cost for current position and personal best
             self.swarm.current_cost = objective_func(
                 self.swarm.position, **kwargs
             )
-            self.swarm.pbest_cost = objective_func(
-                self.swarm.pbest_pos, **kwargs
-            )
             self.swarm.pbest_pos, self.swarm.pbest_cost = compute_pbest(
                 self.swarm
             )

pyswarms/single/general_optimizer.py

@@ -203,11 +203,11 @@ def optimize(self, objective_func, iters, fast=False, **kwargs):
             lvl=logging.INFO,
         )
 
+        self.swarm.pbest_cost = np.full(self.swarm_size[0], np.inf)
         for i in self.rep.pbar(iters, self.name):
             # Compute cost for current position and personal best
             # fmt: off
             self.swarm.current_cost = objective_func(self.swarm.position, **kwargs)
-            self.swarm.pbest_cost = objective_func(self.swarm.pbest_pos, **kwargs)
             self.swarm.pbest_pos, self.swarm.pbest_cost = compute_pbest(self.swarm)
             best_cost_yet_found = self.swarm.best_cost
             # fmt: on
@@ -241,7 +241,9 @@ def optimize(self, objective_func, iters, fast=False, **kwargs):
             )
         # Obtain the final best_cost and the final best_position
         final_best_cost = self.swarm.best_cost.copy()
-        final_best_pos = self.swarm.position[self.swarm.pbest_cost.argmin()].copy()
+        final_best_pos = self.swarm.position[
+            self.swarm.pbest_cost.argmin()
+        ].copy()
         # Write report in log and return final cost and position
         self.rep.log(
             "Optimization finished | best cost: {}, best pos: {}".format(

pyswarms/single/global_best.py

@@ -162,13 +162,13 @@ def optimize(self, objective_func, iters, fast=False, **kwargs):
             lvl=logging.INFO,
         )
 
+        self.swarm.pbest_cost = np.full(self.swarm_size[0], np.inf)
         for i in self.rep.pbar(iters, self.name):
             if not fast:
                 sleep(0.01)
             # Compute cost for current position and personal best
             # fmt: off
             self.swarm.current_cost = objective_func(self.swarm.position, **kwargs)
-            self.swarm.pbest_cost = objective_func(self.swarm.pbest_pos, **kwargs)
             self.swarm.pbest_pos, self.swarm.pbest_cost = compute_pbest(self.swarm)
             # Set best_cost_yet_found for ftol
             best_cost_yet_found = self.swarm.best_cost

pyswarms/single/local_best.py

@@ -186,16 +186,14 @@ def optimize(self, objective_func, iters, fast=False, **kwargs):
             lvl=logging.INFO,
         )
 
+        self.swarm.pbest_cost = np.full(self.swarm_size[0], np.inf)
         for i in self.rep.pbar(iters, self.name):
             if not fast:
                 sleep(0.01)
             # Compute cost for current position and personal best
             self.swarm.current_cost = objective_func(
                 self.swarm.position, **kwargs
             )
-            self.swarm.pbest_cost = objective_func(
-                self.swarm.pbest_pos, **kwargs
-            )
             self.swarm.pbest_pos, self.swarm.pbest_cost = compute_pbest(
                 self.swarm
             )
@@ -230,7 +228,9 @@ def optimize(self, objective_func, iters, fast=False, **kwargs):
             )
         # Obtain the final best_cost and the final best_position
         final_best_cost = self.swarm.best_cost.copy()
-        final_best_pos = self.swarm.position[self.swarm.pbest_cost.argmin(axis=0)].copy()
+        final_best_pos = self.swarm.position[
+            self.swarm.pbest_cost.argmin(axis=0)
+        ].copy()
         # Write report in log and return final cost and position
         self.rep.log(
             "Optimization finished | best cost: {}, best pos: {}".format(

tests/backend/topology/test_pyramid.py

@@ -28,7 +28,9 @@ def test_compute_gbest_return_values(
         """Test if compute_gbest() gives the expected return values"""
         topo = topology(static=static)
         expected_cost = 1.0002528364353296
-        expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
+        expected_pos = np.array(
+            [9.90438476e-01, 2.50379538e-03, 1.87405987e-05]
+        )
         pos, cost = topo.compute_gbest(swarm, **options)
         assert cost == pytest.approx(expected_cost)
         assert pos[np.argmin(cost)] == pytest.approx(expected_pos)

tests/backend/topology/test_random.py

@@ -29,12 +29,17 @@ def test_compute_gbest_return_values(
         """Test if update_gbest_neighborhood gives the expected return values"""
         topo = topology(static=static)
         expected_cost = 1.0002528364353296
-        expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
-        expected_pos_2 = np.array([9.98033031e-01, 4.97392619e-03, 3.07726256e-03])
+        expected_pos = np.array(
+            [9.90438476e-01, 2.50379538e-03, 1.87405987e-05]
+        )
+        expected_pos_2 = np.array(
+            [9.98033031e-01, 4.97392619e-03, 3.07726256e-03]
+        )
         pos, cost = topo.compute_gbest(swarm, **options)
         assert cost == pytest.approx(expected_cost)
-        assert ((pos[np.argmin(cost)] == pytest.approx(expected_pos)) or
-        (pos[np.argmin(cost)] == pytest.approx(expected_pos_2)))
+        assert (pos[np.argmin(cost)] == pytest.approx(expected_pos)) or (
+            pos[np.argmin(cost)] == pytest.approx(expected_pos_2)
+        )
 
     @pytest.mark.parametrize("static", [True, False])
     @pytest.mark.parametrize("k", [1, 2])

tests/backend/topology/test_ring.py

@@ -30,8 +30,13 @@ def test_compute_gbest_return_values(self, swarm, topology, p, k, static):
         topo = topology(static=static)
         pos, cost = topo.compute_gbest(swarm, p=p, k=k)
         expected_cost = 1.0002528364353296
-        expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
-        expected_pos_2 = np.array([9.98033031e-01, 4.97392619e-03, 3.07726256e-03])
+        expected_pos = np.array(
+            [9.90438476e-01, 2.50379538e-03, 1.87405987e-05]
+        )
+        expected_pos_2 = np.array(
+            [9.98033031e-01, 4.97392619e-03, 3.07726256e-03]
+        )
         assert cost == pytest.approx(expected_cost)
-        assert ((pos[np.argmin(cost)] == pytest.approx(expected_pos)) or
-                (pos[np.argmin(cost)] == pytest.approx(expected_pos_2)))
+        assert (pos[np.argmin(cost)] == pytest.approx(expected_pos)) or (
+            pos[np.argmin(cost)] == pytest.approx(expected_pos_2)
+        )

tests/backend/topology/test_von_neumann.py

@@ -27,12 +27,17 @@ def test_update_gbest_neighborhood(self, swarm, topology, p, r):
         """Test if update_gbest_neighborhood gives the expected return values"""
         topo = topology()
         pos, cost = topo.compute_gbest(swarm, p=p, r=r)
-        expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
-        expected_pos_2 = np.array([9.98033031e-01, 4.97392619e-03, 3.07726256e-03])
+        expected_pos = np.array(
+            [9.90438476e-01, 2.50379538e-03, 1.87405987e-05]
+        )
+        expected_pos_2 = np.array(
+            [9.98033031e-01, 4.97392619e-03, 3.07726256e-03]
+        )
         expected_cost = 1.0002528364353296
         assert cost == pytest.approx(expected_cost)
-        assert ((pos[np.argmin(cost)] == pytest.approx(expected_pos)) or
-                (pos[np.argmin(cost)] == pytest.approx(expected_pos_2)))
+        assert (pos[np.argmin(cost)] == pytest.approx(expected_pos)) or (
+            pos[np.argmin(cost)] == pytest.approx(expected_pos_2)
+        )
 
     @pytest.mark.parametrize("m", [i for i in range(3)])
     @pytest.mark.parametrize("n", [i for i in range(3)])