enhanced-political-optimizer 1.0.0

Solve global optimization problems.

How to use the package.

To use the package you need to provide four things to the PO methods

• Optimization function, which the is function that you are trying to optimize. Our method can cater to constraints as well by using static penalty. An example is given below.
• Number of dimensions of the problem
• Lower boundary
• Upper boundary

Note The boundaries can be scalars or arrays. The example below uses arrays because the boundaries are different for different variables, if they are same, use scalar values.

import numpy as np
import matplotlib.pyplot as plt


## optimization function.
def TCSD(x: np.ndarray):
    x1 = x[0]
    x2 = x[1]
    x3 = x[2]
    z = (x3 + 2) * x2 * (x1 * x1)

    c = [
        1 - ((x2 * x2 * x2 * x3)) / (71785 * x1 * x1 * x1 * x1),
        ((4 * x2 * x2 - (x1 * x2)) / (12566 * (x2 * x1 * x1 * x1 - (x1 ** 4))))
        + (1 / (5108 * x1 * x1))
        - 1,
        1 - ((140.45 * x1) / (x2 * x2 * x3)),
        ((x1 + x2) / 1.5) - 1,
    ]
    cmaxes = [x if x > 0 else 0 for x in c]
    c_np_array = np.array(cmaxes)
    c_np_array = np.power(c_np_array, 2)
    c_np_array = (10 ** 10) * c_np_array
    sumofarray = np.sum(c_np_array)
    return z + sumofarray


scores = [0 for x in range(RUNS)]

for i in range(RUNS):
    # matlab's rng("shuffle") seeds with the system's default time, python's random.seed has that as default val
    np.random.seed(1)
    # implement political optimizer.

    objectiveFunction = TCSD
    dim = 3 # dimentions
    lb = [0.05, 0.25, 2.00] # upper boundary
    ub = [2.00, 1.30, 15.00] # lower boundary
    (leaderScore, leaderPosition, convergenceCurve) = PO(lb, ub, dim, objectiveFunction)
    print(leaderScore)
    print(leaderPosition)