Fix optimise methods returning incorrect best_pos

pyswarms/discrete/binary.py

@@ -202,7 +202,7 @@ def optimize(self, objective_func, iters, **kwargs):
             self.swarm.position = self._compute_position(self.swarm)
         # 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.pbest_pos[self.swarm.pbest_cost.argmin()].copy()
         self.rep.log(
             "Optimization finished | best cost: {}, best pos: {}".format(
                 final_best_cost, final_best_pos

pyswarms/single/general_optimizer.py

@@ -248,7 +248,7 @@ def optimize(self, objective_func, iters, **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.pbest_pos[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

@@ -207,7 +207,7 @@ def optimize(self, objective_func, iters, **kwargs):
             )
         # Obtain the final best_cost and the final best_position
         final_best_cost = self.swarm.best_cost.copy()
-        final_best_pos = self.swarm.best_pos.copy()
+        final_best_pos = self.swarm.pbest_pos[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/local_best.py

@@ -235,7 +235,7 @@ def optimize(self, objective_func, iters, **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.pbest_pos[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(

tests/optimizers/test_binary.py

@@ -3,6 +3,7 @@
 
 # Import modules
 import pytest
+import numpy as np
 
 # Import from pyswarms
 from pyswarms.discrete import BinaryPSO
@@ -28,3 +29,13 @@ def optimizer_reset(self, options):
         opt.optimize(sphere, 10)
         opt.reset()
         return opt
+
+    def test_binary_correct_pos(self, options):
+        """ Test to check binary optimiser returns the correct position
+        corresponding to the best cost """
+        opt = BinaryPSO(10, 2, options=options)
+        cost, pos = opt.optimize(sphere, 10)
+        # find best pos from history
+        min_cost_idx = np.argmin(opt.cost_history)
+        min_pos_idx = np.argmin(sphere(opt.pos_history[min_cost_idx]))
+        assert np.array_equal(opt.pos_history[min_cost_idx][min_pos_idx], pos)

tests/optimizers/test_general_optimizer.py

@@ -95,3 +95,11 @@ def test_obj_incorrect_kwargs(self, obj_with_args, optimizer):
         with pytest.raises(TypeError):
             # Wrong kwargs
             cost, pos = optimizer.optimize(obj_with_args, 1000, c=1, d=100)
+
+    def test_general_correct_pos(self, options, optimizer):
+        """ Test to check general optimiser returns the correct position corresponding to the best cost """
+        cost, pos = optimizer.optimize(sphere, iters=5)
+        # find best pos from history
+        min_cost_idx = np.argmin(optimizer.cost_history)
+        min_pos_idx = np.argmin(sphere(optimizer.pos_history[min_cost_idx]))
+        assert np.array_equal(optimizer.pos_history[min_cost_idx][min_pos_idx], pos)

tests/optimizers/test_global_best.py

@@ -3,6 +3,7 @@
 
 # Import modules
 import pytest
+import numpy as np
 
 # Import from pyswarms
 from pyswarms.single import GlobalBestPSO
@@ -28,3 +29,12 @@ def optimizer_reset(self, options):
         opt.optimize(sphere, 10)
         opt.reset()
         return opt
+
+    def test_global_correct_pos(self, options):
+        """ Test to check global optimiser returns the correct position corresponding to the best cost """
+        opt = GlobalBestPSO(n_particles=10, dimensions=2, options=options)
+        cost, pos = opt.optimize(sphere, iters=5)
+        # find best pos from history
+        min_cost_idx = np.argmin(opt.cost_history)
+        min_pos_idx = np.argmin(sphere(opt.pos_history[min_cost_idx]))
+        assert np.array_equal(opt.pos_history[min_cost_idx][min_pos_idx], pos)

tests/optimizers/test_local_best.py

@@ -3,6 +3,7 @@
 
 # Import modules
 import pytest
+import numpy as np
 
 # Import from pyswarms
 from pyswarms.single import LocalBestPSO
@@ -28,3 +29,12 @@ def optimizer_reset(self, options):
         opt.optimize(sphere, 10)
         opt.reset()
         return opt
+
+    def test_local_correct_pos(self, options):
+        """ Test to check local optimiser returns the correct position corresponding to the best cost """
+        opt = LocalBestPSO(n_particles=10, dimensions=2, options=options)
+        cost, pos = opt.optimize(sphere, iters=5)
+        # find best pos from history
+        min_cost_idx = np.argmin(opt.cost_history)
+        min_pos_idx = np.argmin(sphere(opt.pos_history[min_cost_idx]))
+        assert np.array_equal(opt.pos_history[min_cost_idx][min_pos_idx], pos)