work in progress

book/_toc.yml

@@ -28,4 +28,3 @@ parts:
     - file: pages/metaheuristics
       sections:
       - file: pages/metaheuristics_example
-    - file: more_will_follow

book/pages/metaheuristics_example.ipynb

@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "d4f3fe4b",
    "metadata": {
     "tags": [
@@ -196,18 +196,86 @@
    "id": "c1e8ff92",
    "metadata": {},
    "source": [
-    "#### Define variables\n",
-    "As before, we don't need to specify our variable $x$ itself as defined in {eq}`meta_x`. "
+    "### Define variables\n",
+    "As before, we don't need to specify our variable $x$ itself as defined in {eq}`meta_x`. However, we do need to specify that we have integers (specifically only $0$ and $1$ which will be covered by the bounds)\n",
+    "\n",
+    "#### SciPy\n",
+    "In SciPy we use an array of booleans to specify which design variables are integers: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c46446d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "integers=np.array([True,True,True,True,True,True,True,True,True,True])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d10375a7",
+   "metadata": {},
+   "source": [
+    "#### pymoo\n",
+    "In pymoo we don't have to specify that have integers, but we have to adapt all of our functions later on so that the outputs are integers.\n",
+    "\n",
+    "### Define bounds\n",
+    "Now let's continue with the bounds, specified by {eq}`meta_x` too:\n",
+    "\n",
+    "#### SciPy\n",
+    "The bounds are defined as before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f422f0d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bounds = [[0,1],\n",
+    "          [0,1],\n",
+    "          [0,1],\n",
+    "          [0,1],\n",
+    "          [0,1],\n",
+    "          [0,1],\n",
+    "          [0,1],\n",
+    "          [0,1],\n",
+    "          [0,1],\n",
+    "          [0,1]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b444641c",
+   "metadata": {},
+   "source": [
+    "#### pymoo\n",
+    "\n",
+    "In pymoo, the bounds are defined as separate arrays:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f740b252",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xl = np.array([0,0,0,0,0,0,0,0,0,0])\n",
+    "xu = np.array([1,1,1,1,1,1,1,1,1,1])"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "1333c1bb",
    "metadata": {},
    "source": [
-    "#### Define objective function\n",
+    "### Define objective function\n",
     "\n",
-    "Let's define the objective function as defined in {eq}`meta_c`:"
+    "Let's define the objective function as defined in {eq}`meta_c`."
    ]
   },
   {
@@ -221,6 +289,59 @@
     "    return np.array([500, 800, 1000, 1500, 4000, 5700, 6500, 5400, 5500, 6800])@x"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "630cf95d",
+   "metadata": {},
+   "source": [
+    "### Define constraint function\n",
+    "\n",
+    "Let's define the constraint function as defined in {eq}`meta_g_matrix` and {eq}`meta_h`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4173993",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g = np.array([-200, -240, -265, -330, -470, -700, -800, -640, -730, -775])\n",
+    "\n",
+    "def nonlinconfun(x):\n",
+    "    return max(x[4:7]) * max(x[7:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8500036",
+   "metadata": {},
+   "source": [
+    "#### SciPy\n",
+    "\n",
+    "In SciPy we need to store the functions in a scipy-object including the bounds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e35b955a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lincon = sp.optimize.LinearConstraint(Ei, lb=np.inf, ub=2700)\n",
+    "nonlincon = sp.optimize.NonlinearConstraint(nonlinconfun, 0, 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7635e24b",
+   "metadata": {},
+   "source": [
+    "#### pymoo\n",
+    "In pymoo the functions can be inserted directly in the problem object later on."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "76ddef0f",
@@ -234,9 +355,9 @@
    "id": "18dba09c",
    "metadata": {},
    "source": [
-    "Now let's solve the problem for each of the three models. We're gonna time them to see how long the analysis takes. Please note that when running this in the browser, the `CPU times` gives invalid values.\n",
+    "Now let's solve the problem using both SciPy and pymoo.\n",
     "\n",
-    "#### Weighted objective function"
+    "#### SciPy"
    ]
   },
   {
@@ -266,8 +387,9 @@
    ],
    "source": [
     "%%time\n",
-    "result = sp.optimize.minimize(fun = weighted_obj, x0 = x0, bounds = bounds)\n",
-    "print(result)"
+    "result = sp.optimize.differential_evolution(func = objective,bounds = bounds,constraints=[lincon,nonlincon], integrality = integers)\n",
+    "print(result)\n",
+    "print([email protected])"
    ]
   },
   {
@@ -1865,7 +1987,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,