Update examples

examples/applications/discrete-problems/knapsack_problem.ipynb

@@ -2,7 +2,11 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "# Knapsack Problem\n",
     "\n",
@@ -12,23 +16,29 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
     "pycharm": {
      "name": "#%%\n"
     }
    },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "# Solver\n",
     "\n",
     "We gonna use WOA to solve this problem\n"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   },
   {
    "cell_type": "code",
@@ -211,7 +221,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": []
   }

examples/applications/gear_box/gear_box_optimization.py

@@ -1,8 +1,6 @@
 
 from tkinter import W
-from mealpy.swarm_based.WOA import OriginalWOA
-from mealpy.swarm_based.GOA import OriginalGOA
-from mealpy.swarm_based.GWO import OriginalGWO
+from mealpy import FloatVar, WOA, GOA, GWO
 import numpy as np
 
 """
@@ -85,12 +83,11 @@ def violate(value):
     N_1 = 1500
     P = 7.5
 
-    problem_dict1 = {
-        "fit_func": gear_box,
-        "lb": [20, 10, 30, 18, 2.75],
-        "ub": [32, 30, 40, 25, 4],
-        "minmax": "min",
+    problem = {
+        "obj_func": gear_box,
+        "bounds": FloatVar(lb=[20, 10, 30, 18, 2.75], ub=[32, 30, 40, 25, 4]),
+        "minmax": "min"
     }
-    model1 = OriginalWOA(epoch=1000, pop_size=500)
-    best_position, best_fitness = model1.solve(problem_dict1)
-    print(f"Best solution: {best_position}, Best fitness: {best_fitness}")
+    model1 = WOA.OriginalWOA(epoch=1000, pop_size=500)
+    gbest = model1.solve(problem)
+    print(f"Best solution: {gbest.solution}, Best fitness: {gbest.target.fitness}")

examples/applications/keras/mha-hybrid-mlp-classification.py

@@ -6,13 +6,11 @@
 
 # https://machinelearningmastery.com/display-deep-learning-model-training-history-in-keras/
 
-
 import numpy as np
 from keras.models import Sequential
 from keras.layers import Dense
 from sklearn.metrics import accuracy_score
-from mealpy.swarm_based import GWO
-from mealpy.evolutionary_based import FPA
+from mealpy import GWO, FloatVar
 
 
 class HybridMlp:
@@ -40,12 +38,10 @@ def create_network(self):
 
     def create_problem(self):
         self.problem = {
-            "fit_func": self.fitness_function,
-            "lb": [-1, ] * self.n_dims,
-            "ub": [1, ] * self.n_dims,
+            "obj_func": self.fitness_function,
+            "bounds": FloatVar(lb=(-1.,)*self.n_dims, ub=(1.,)*self.n_dims),
             "minmax": "max",
-            "log_to": None,
-            "save_population": False
+            "log_to": "console",
         }
 
     def decode_solution(self, solution):

examples/applications/keras/mha-hybrid-mlp-time-series.py

@@ -18,8 +18,7 @@
 import numpy as np
 from keras.models import Sequential
 from keras.layers import Dense
-from mealpy.swarm_based import GWO
-from mealpy.evolutionary_based import FPA
+from mealpy import FloatVar, FPA
 from permetrics.regression import RegressionMetric
 
 
@@ -61,12 +60,10 @@ def create_problem(self):
         self.n_inputs = self.X_train.shape[1]
         self.n_dims = (self.n_inputs * self.n_hidden_nodes) + self.n_hidden_nodes + (self.n_hidden_nodes * 1) + 1
         self.problem = {
-            "fit_func": self.fitness_function,
-            "lb": [-1, ] * self.n_dims,
-            "ub": [1, ] * self.n_dims,
+            "obj_func": self.fitness_function,
+            "bounds": FloatVar(lb=(-1.,)*self.n_dims, ub=(1.0,)*self.n_dims),
             "minmax": "min",
             "obj_weights": [0.3, 0.2, 0.5],  # [mae, mse, rmse]
-            "save_population": False,
         }
 
     def prediction(self, solution, data):
@@ -136,4 +133,3 @@ def fitness_function(self, solution):
 x_input = x_input.reshape((1, n_steps))
 yhat = model.prediction(model.solution, x_input)
 print(yhat)
-

examples/run_multitask.ipynb

@@ -1,19 +1,22 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "# Scenarios\n",
     "1. Run 1 algorithm with 1 problem, and multiple trials \n",
     "2. Run 1 algorithm with multiple problems, and multiple trials\n",
     "3. Run multiple algorithms with 1 problem, and multiple trials\n",
     "4. Run multiple algorithms with multiple problems, and multiple trials"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   },
   {
    "cell_type": "code",

examples/run_multitask.py

@@ -56,5 +56,5 @@
 if __name__ == "__main__":
     multitask = Multitask(algorithms=(model1, model2, model3, model4), problems=(p1, p2, p3), terminations=(term, ), modes=("thread", ), n_workers=4)
     # default modes = "single", default termination = epoch (as defined in problem dictionary)
-    multitask.execute(n_trials=5, n_jobs=None, save_path="history", save_as="csv", save_convergence=True, verbose=False)
+    multitask.execute(n_trials=5, n_jobs=None, save_path="history7", save_as="csv", save_convergence=True, verbose=False)
     # multitask.execute(n_trials=5, save_path="history", save_as="csv", save_convergence=True, verbose=False)