Updated line 694 and 700 with: p[0] = x and p[1] = y

app.py

@@ -691,13 +691,13 @@ iface = gr.Interface(
     The objective function to be optimized is given by the following formula:
     
     ```math
-    f(p) = -5.0 \cdot \exp \left( -0.5 \cdot \left( \frac{(p[0]+2.2)^2}{0.4} + \frac{(p[1]-4.3)^2}{0.4} \right) \right) + -2.0 \cdot \exp \left( -0.5 \cdot \left( \frac{(p[0]-2.2)^2}{0.4} + \frac{(p[1]+4.3)^2}{0.4} \right) \right)
+    f(p) = -5.0 \cdot \exp \left( -0.5 \cdot \left( \frac{(x+2.2)^2}{0.4} + \frac{(y-4.3)^2}{0.4} \right) \right) + -2.0 \cdot \exp \left( -0.5 \cdot \left( \frac{(x-2.2)^2}{0.4} + \frac{(y+4.3)^2}{0.4} \right) \right)
     ```
     
     Or in a more readable form:
     
     $$
-    f(p) = -5.0 \cdot \exp \left( -0.5 \cdot \left( \frac{(p[0]+2.2)^2}{0.4} + \frac{(p[1]-4.3)^2}{0.4} \right) \right) + -2.0 \cdot \exp \left( -0.5 \cdot \left( \frac{(p[0]-2.2)^2}{0.4} + \frac{(p[1]+4.3)^2}{0.4} \right) \right)
+    f(p) = -5.0 \cdot \exp \left( -0.5 \cdot \left( \frac{(x+2.2)^2}{0.4} + \frac{(y-4.3)^2}{0.4} \right) \right) + -2.0 \cdot \exp \left( -0.5 \cdot \left( \frac{(x-2.2)^2}{0.4} + \frac{(y+4.3)^2}{0.4} \right) \right)
     $$
     """,
     article="## Grey Wolf Optimizer\nThe Grey Wolf Optimizer (GWO) is a population-based metaheuristic optimization algorithm inspired by the social behavior of grey wolves in nature."