soft_computing_project.py

# -*- coding: utf-8 -*-
"""Soft Computing Project

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1ou3XzPECyEjCs0gIAsiHxzWhKpgn6epE
"""

import random


NUM_DEVICES = int(input("Enter number of devices: "))  # Number of devices


energy_cost = []
print("Enter energy cost for each device:")   # Here we are finding the energy cost for each device.
for i in range(NUM_DEVICES):
    cost = float(input(f"Device {i + 1} cost: "))
    energy_cost.append(cost)

# Creating an individual (device usage pattern) with at least one device ON.
def create_individual():
    while True:
        ind = [random.randint(0, 1) for _ in range(NUM_DEVICES)]
        if sum(ind) >= 2:  # Require at least 2 devices ON
            return ind

# Creating the population
def create_population(size):
    return [create_individual() for _ in range(size)]

# Calculating fitness
def fitness(individual):
    total_cost = sum([individual[i] * energy_cost[i] for i in range(NUM_DEVICES)])
    num_on = sum(individual)
    if num_on < 2:
        return 0 # Apply penalty if the number of active devices is less than 2.
    return (num_on + 1) / (total_cost + 1)

# Selection
def selection(population):
    weights = [fitness(ind) for ind in population]
    return random.choices(population, weights=weights, k=2)

# Crossover
def crossover(parent1, parent2):
    point = random.randint(1, NUM_DEVICES - 1)
    return parent1[:point] + parent2[point:]

# Mutation
def mutate(individual, mutation_rate=0.3):
    return [1 - gene if random.random() < mutation_rate else gene for gene in individual]

# Run the Genetic Algorithm
def genetic_algorithm(generations=50, population_size=100):
    population = create_population(population_size)

    for generation in range(generations):
        new_population = []

        for _ in range(population_size):
            parent1, parent2 = selection(population)
            child = crossover(parent1, parent2)
            child = mutate(child)
            if sum(child) >= 2:  # Checking at least 2 devices ON
                new_population.append(child)
            else:
                new_population.append(create_individual())

        population = new_population

        best = max(population, key=fitness)
        print(f"Generation {generation + 1}: Best Fitness = {fitness(best):.4f}")

    best_individual = max(population, key=fitness)
    best_cost = sum([best_individual[i] * energy_cost[i] for i in range(NUM_DEVICES)])

    print("\n✅ Best Device Usage Pattern (1=On, 0=Off):", best_individual)
    print("💡 Corresponding Energy Cost:", best_cost)

# Run the program
genetic_algorithm()