Update examples

thieu1995 · Sep 26, 2023 · 766680d · 766680d
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diff --git a/README.md b/README.md
@@ -39,7 +39,7 @@ traditional functions with different dimensions are implemented.
 ### Install with pip
 
 Install the [current PyPI release](https://pypi.python.org/pypi/opfunu):
-```sh 
+```sh
 $ pip install opfunu==1.0.1
 ```
 
@@ -61,7 +61,7 @@ $ python setup.py install
 
 ## Lib's structure
 
-```code 
+```code
 
 docs
 examples
@@ -170,7 +170,7 @@ For more usage examples please look at [examples](/examples) folder.
 
 If you are using opfunu in your project, we would appreciate citations:
 
-```code 
+```code
 @software{thieu_nguyen_2020_3711682,
   author       = {Nguyen Van Thieu},
   title        = {Opfunu: An Open-source Python Library for Optimization Benchmark Functions},
@@ -184,13 +184,11 @@ If you are using opfunu in your project, we would appreciate citations:
 
 ## References 
 
-```code 
-
+```code
 1. http://benchmarkfcns.xyz/fcns
 2. https://en.wikipedia.org/wiki/Test_functions_for_optimization
 3. https://www.cs.unm.edu/~neal.holts/dga/benchmarkFunction/
 4. http://www.sfu.ca/~ssurjano/optimization.html
 5. A Literature Survey of Benchmark Functions For Global Optimization Problems (2013)
 6. Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization 
-
 ```
diff --git a/examples/example_with_mealpy.py b/examples/example_with_mealpy.py
@@ -12,16 +12,16 @@
 
 problem_dict1 = {
     "fit_func": ackey.evaluate,
-    "lb": ackey.lb.tolist(),
-    "ub": ackey.ub.tolist(),
+    "lb": ackey.lb,
+    "ub": ackey.ub,
     "minmax": "min",
     "log_to": None,
     "save_population": False,
 }
 
 ## Run the algorithm
-model = SMA.BaseSMA(problem_dict1, epoch=100, pop_size=50, pr=0.03)
-best_position, best_fitness = model.solve()
+model = SMA.BaseSMA(epoch=100, pop_size=50, pr=0.03)
+best_position, best_fitness = model.solve(problem_dict1)
 print(f"Best solution: {best_position}, Best fitness: {best_fitness}")
 
 print(ackey.n_fe)

diff --git a/examples/name_based/c_func.py b/examples/name_based/c_func.py
@@ -202,18 +202,14 @@
 print(problem.is_succeed(problem.x_global))
 
 
-
-# problem_dict = {
-#     "fit_func": problem.evaluate,
-#     "lb": problem.lb.tolist(),
-#     "ub": problem.ub.tolist(),
-#     "minmax": "min",
-#     "log_to": "None",
-# }
-#
-# model = WOA.BaseWOA(problem_dict, epoch=1000, pop_size=50)
-# best_position, best_fitness_value = model.solve()
-# print(best_position, best_fitness_value)
-#
-
-
+problem_dict = {
+    "fit_func": problem.evaluate,
+    "lb": problem.lb.tolist(),
+    "ub": problem.ub.tolist(),
+    "minmax": "min",
+    "log_to": "None",
+}
+
+model = WOA.OriginalWOA(epoch=1000, pop_size=50)
+best_position, best_fitness_value = model.solve(problem_dict)
+print(best_position, best_fitness_value)
diff --git a/examples/name_based/d_func.py b/examples/name_based/d_func.py
@@ -153,15 +153,14 @@
 print(problem.is_succeed(problem.x_global))
 
 
-# problem_dict = {
-#     "fit_func": problem.evaluate,
-#     "lb": problem.lb.tolist(),
-#     "ub": problem.ub.tolist(),
-#     "minmax": "min",
-#     "log_to": "None",
-# }
-#
-# model = WOA.BaseWOA(problem_dict, epoch=1000, pop_size=50)
-# best_position, best_fitness_value = model.solve()
-# print(best_position, best_fitness_value)
-
+problem_dict = {
+    "fit_func": problem.evaluate,
+    "lb": problem.lb,
+    "ub": problem.ub,
+    "minmax": "min",
+    "log_to": "None",
+}
+
+model = WOA.OriginalWOA(epoch=1000, pop_size=50)
+best_position, best_fitness_value = model.solve(problem_dict)
+print(best_position, best_fitness_value)
diff --git a/examples/name_based/e_func.py b/examples/name_based/e_func.py
@@ -93,19 +93,14 @@
 print(problem.is_succeed(problem.x_global))
 
 
-# problem_dict = {
-#     "fit_func": problem.evaluate,
-#     "lb": problem.lb.tolist(),
-#     "ub": problem.ub.tolist(),
-#     "minmax": "min",
-#     "log_to": "None",
-# }
-#
-# model = WOA.BaseWOA(problem_dict, epoch=1000, pop_size=50)
-# best_position, best_fitness_value = model.solve()
-# print(best_position, best_fitness_value)
-
-
-
-
-
+problem_dict = {
+    "fit_func": problem.evaluate,
+    "lb": problem.lb,
+    "ub": problem.ub,
+    "minmax": "min",
+    "log_to": "None",
+}
+
+model = WOA.OriginalWOA(epoch=1000, pop_size=50)
+best_position, best_fitness_value = model.solve(problem_dict)
+print(best_position, best_fitness_value)