PCB_Layout_Optimization/main.py at main · LorenzoTonet/PCB_Layout_Optimization · GitHub

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from PCB_class import PCB
from Component_class import Component, Pin
from Plots import plot_pcb
from NSGA_II_implementation import *
from Genetic_algorithms import *

import random as rnd

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

if __name__ == "__main__":

    pin1_c1 = Pin(id="P1", relative_x=-5, relative_y=0)
    pin2_c1 = Pin(id="P2", relative_x=5, relative_y=0)

    comp1 = Component(
        id="C1",
        shape="rect",
        size_x=20, size_y=10,
        pins=[pin1_c1, pin2_c1],
        position=(21, 21),
        temp_gradient_params=(100, 15)
    )

    pin3_c2 = Pin(id="P3", relative_x=0, relative_y=-3)
    pin4_c2 = Pin(id="P4", relative_x=0, relative_y=3)

    comp2 = Component(
        id="C2",
        shape="circle",
        size_x=15, size_y=15,
        pins=[pin3_c2, pin4_c2],
        position=(20, 20),
        temp_gradient_params=(100, 15)
    )

    pin5_c3 = Pin(id="P5", relative_x=0, relative_y=-3)

    comp3 = Component(
        id="C3",
        shape="rect",
        size_x=10, size_y=10,
        pins=[pin5_c3],
        position=(20, 20),
        temp_gradient_params=(10, 3)
    )
    links = [
            (("C1", "P2"), ("C2", "P3")),
            (("C3","P5"),("C2","P4")),
            (("C1","P1"), ("C3","P5"))
            ]

    pcb1 = PCB(
        max_width=50,
        max_height=50,
        components=[comp1, comp2, comp3],
        links=links
    )
    pcb1.random_placement()
    pcb1.resolve_conflicts(max_iterations=100)

    # Genetic Algorithm parameters

    number_of_generations = 50
    population_size = 100
    rotation_mutation_rate = 0.4
    position_mutation_rate = 0.1
    elitism_count = 10

    #generation of a random population
    pop = generate_random_population(pcb1, population_size)

    for generation in range(number_of_generations):

        pop_objectives = [evaluate_objectives(pcb) for pcb in pop]

        fronts, ranks = fast_non_dominated_sort(pop_objectives, verbose=False)
        crowding = calculate_crowding_distance_for_population(pop, pop_objectives, fronts)

        offspring = []
        while len(offspring) < population_size:

            # selection of parents (via rank and then crowding distance)
            p1 = tournament_select(pop, ranks, crowding)
            p2 = tournament_select(pop, ranks, crowding)

            # crossover (swap 1 component between parents)
            child1, child2 = crossover(p1, p2, 1)

            # mutate rotation (less impactful) and position (very impactful)
            mutate_rotation(child1, rotation_mutation_rate)
            mutate_position(child1, position_mutation_rate)
            mutate_rotation(child2, rotation_mutation_rate)
            mutate_position(child2, position_mutation_rate)

            offspring.append(child1)
            offspring.append(child2)

        offspring = offspring[:population_size]


        offspring_objectives = [evaluate_objectives(pcb) for pcb in offspring]

        # elitism
        mixed_pop = pop + offspring
        mixed_obj = pop_objectives + offspring_objectives

        # select the next generation
        pop, _ = nsga2_select(mixed_pop, mixed_obj, population_size)

        plot_pcb(rnd.sample(pop, 1)[0], show_temp=True)


        pop_results_objectives = [evaluate_objectives(pcb) for pcb in pop]
        random_pop = generate_random_population(pcb1, population_size)
        random_pop_results_objectives = [evaluate_objectives(pcb) for pcb in random_pop]

        fig = plt.figure()
        ax = fig.add_subplot(111, projection='3d')

        ax.scatter(
            [obj[0] for obj in random_pop_results_objectives],   # max_temp
            [obj[1] for obj in random_pop_results_objectives],   # occupied_area
            [obj[2] for obj in random_pop_results_objectives],   # pin_distance
            color='red',
            label='Random Population'
        )

        ax.scatter(
            [obj[0] for obj in pop_results_objectives],
            [obj[1] for obj in pop_results_objectives],
            [obj[2] for obj in pop_results_objectives],
            color='blue',
            label='Evolved Population'
        )

        ax.set_xlabel('Max temperature')
        ax.set_ylabel('Total area')
        ax.set_zlabel('Pin distance')

        ax.legend()
        plt.show()