Merge branch 'master' of github.com:thieu1995/mealpy

docs/requirements.txt

@@ -1,7 +1,7 @@
 # Defining the exact version will make sure things don't break
 sphinx==4.4.0
 sphinx_rtd_theme==1.0.0
-readthedocs-sphinx-search==0.1.1
+readthedocs-sphinx-search==0.3.2
 numpy>=1.17.5
 matplotlib==3.1.3
 pandas>=1.2.0

examples/utils/visualize/convergence_chart.py

@@ -21,7 +21,7 @@ def fitness_function(solution):
 
 problem = {
     "fit_func": fitness_function,
-    "bounds": FloatVar(n_vars=8, lb=[-10, -5, -15, -20, -10, -15, -10, -30], ub=[10, 5, 15, 20, 50, 30, 100, 85]),
+    "bounds": FloatVar(lb=[-10, -5, -15, -20, -10, -15, -10, -30], ub=[10, 5, 15, 20, 50, 30, 100, 85]),
     "minmax": "min",
     "obj_weights": [0.2, 0.5, 0.3]
 }

examples/utils/visualize/exploration_exploitation_chart.py

@@ -21,7 +21,7 @@ def fitness_function(solution):
 
 problem = {
     "fit_func": fitness_function,
-    "bounds": FloatVar(n_vars=8, lb=[-10, -5, -15, -20, -10, -15, -10, -30], ub=[10, 5, 15, 20, 50, 30, 100, 85]),
+    "bounds": FloatVar(lb=[-10, -5, -15, -20, -10, -15, -10, -30], ub=[10, 5, 15, 20, 50, 30, 100, 85]),
     "minmax": "min",
     "obj_weights": [0.2, 0.5, 0.3]
 }

examples/utils/visualize/objectives_chart.py

@@ -20,7 +20,7 @@ def fitness_function(solution):
 
 problem = {
     "fit_func": fitness_function,
-    "bounds": FloatVar(n_vars=8, lb=[-10, -5, -15, -20, -10, -15, -10, -30], ub=[10, 5, 15, 20, 50, 30, 100, 85]),
+    "bounds": FloatVar(lb=[-10, -5, -15, -20, -10, -15, -10, -30], ub=[10, 5, 15, 20, 50, 30, 100, 85]),
     "minmax": "min",
     "obj_weights": [0.2, 0.5, 0.3]
 }

examples/utils/visualize/trajectory_chart.py

@@ -20,7 +20,7 @@ def fitness_function(solution):
 
 problem = {
     "fit_func": fitness_function,
-    "bounds": FloatVar(n_vars=8, lb=[-10, -5, -15, -20, -10, -15, -10, -30], ub=[10, 5, 15, 20, 50, 30, 100, 85]),
+    "bounds": FloatVar(lb=[-10, -5, -15, -20, -10, -15, -10, -30], ub=[10, 5, 15, 20, 50, 30, 100, 85]),
     "minmax": "min",
     "obj_weights": [0.2, 0.5, 0.3],
     "save_population": True

mealpy/__init__.py

@@ -13,7 +13,7 @@
 # >>>     return np.sum(solution ** 2)
 # >>>
 # >>> p1 = {
-# >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+# >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
 # >>>     "minmax": "min",
 # >>>     "obj_func": objective_function,
 # >>>     "save_population": True,  # To be able to draw the trajectory figure

mealpy/bio_based/BBO.py

@@ -28,7 +28,7 @@ class OriginalBBO(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }
@@ -119,7 +119,7 @@ class DevBBO(OriginalBBO):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/bio_based/BBOA.py

@@ -24,7 +24,7 @@ class OriginalBBOA(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/bio_based/BMO.py

@@ -27,7 +27,7 @@ class OriginalBMO(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/bio_based/EOA.py

@@ -37,7 +37,7 @@ class OriginalEOA(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/bio_based/IWO.py

@@ -35,7 +35,7 @@ class OriginalIWO(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/bio_based/SBO.py

@@ -33,7 +33,7 @@ class DevSBO(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }
@@ -118,7 +118,7 @@ class OriginalSBO(DevSBO):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/bio_based/SMA.py

@@ -28,7 +28,7 @@ class DevSMA(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }
@@ -123,7 +123,7 @@ class OriginalSMA(DevSMA):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/bio_based/SOA.py

@@ -32,7 +32,7 @@ class DevSOA(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }
@@ -102,7 +102,7 @@ class OriginalSOA(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/bio_based/SOS.py

@@ -23,7 +23,7 @@ class OriginalSOS(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/bio_based/TPO.py

@@ -36,7 +36,7 @@ class DevTPO(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/bio_based/TSA.py

@@ -31,7 +31,7 @@ class OriginalTSA(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/bio_based/VCS.py

@@ -31,7 +31,7 @@ class DevVCS(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }
@@ -157,7 +157,7 @@ class OriginalVCS(DevVCS):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/bio_based/WHO.py

@@ -36,7 +36,7 @@ class OriginalWHO(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/evolutionary_based/CRO.py

@@ -35,7 +35,7 @@ class OriginalCRO(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }
@@ -208,7 +208,7 @@ class OCRO(OriginalCRO):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/evolutionary_based/DE.py

@@ -39,7 +39,7 @@ class OriginalDE(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }
@@ -178,7 +178,7 @@ class JADE(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }
@@ -313,7 +313,7 @@ class SADE(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }
@@ -434,7 +434,7 @@ class SAP_DE(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/evolutionary_based/EP.py

@@ -29,7 +29,7 @@ class OriginalEP(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }
@@ -122,7 +122,7 @@ class LevyEP(OriginalEP):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/evolutionary_based/ES.py

@@ -28,7 +28,7 @@ class OriginalES(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }
@@ -110,7 +110,7 @@ class LevyES(OriginalES):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }
@@ -186,7 +186,7 @@ class CMA_ES(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }
@@ -307,7 +307,7 @@ class Simple_CMA_ES(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/evolutionary_based/FPA.py

@@ -28,7 +28,7 @@ class OriginalFPA(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "minmax": "min",
     >>>     "obj_func": objective_function
     >>> }

mealpy/evolutionary_based/GA.py

@@ -35,7 +35,7 @@ class BaseGA(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "obj_func": objective_function,
     >>>     "minmax": "min",
     >>> }
@@ -348,7 +348,7 @@ class SingleGA(BaseGA):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "obj_func": objective_function,
     >>>     "minmax": "min",
     >>> }
@@ -462,7 +462,7 @@ class EliteSingleGA(SingleGA):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "obj_func": objective_function,
     >>>     "minmax": "min",
     >>> }
@@ -582,7 +582,7 @@ class MultiGA(BaseGA):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "obj_func": objective_function,
     >>>     "minmax": "min",
     >>> }
@@ -682,7 +682,7 @@ class EliteMultiGA(MultiGA):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "obj_func": objective_function,
     >>>     "minmax": "min",
     >>> }

mealpy/evolutionary_based/MA.py

@@ -33,7 +33,7 @@ class OriginalMA(Optimizer):
     >>>     return np.sum(solution**2)
     >>>
     >>> problem_dict = {
-    >>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+    >>>     "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
     >>>     "obj_func": objective_function,
     >>>     "minmax": "min",
     >>> }