Meta_heuristic Algorithm/01-knapstack.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Datasets\n",
    "val = [135, 139, 149, 150, 156, 163, 173, 184, 192, 201, 210, 214, 221, 229, 240]\n",
    "wt = [70, 73, 77, 80, 82, 87, 90, 94, 98, 106, 110, 113, 115, 118, 120]\n",
    "W = 750"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0/1 knapsack problem\n",
    "- Simply recursive\n",
    "- Dynamic programing\n",
    "\n",
    "[geeksforgeeks 0-1-knapsack-problem](https://www.geeksforgeeks.org/0-1-knapsack-problem-dp-10/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A naive recursive implementation of 0-1 Knapsack Problem\n",
    "  \n",
    "# Returns the maximum value that can be put in a knapsack of\n",
    "# capacity W\n",
    "def knapSack(W, wt, val, n):\n",
    "  \n",
    "    # Base Case\n",
    "    if n == 0 or W == 0 :\n",
    "        return 0\n",
    "  \n",
    "    # If weight of the nth item is more than Knapsack of capacity\n",
    "    # W, then this item cannot be included in the optimal solution\n",
    "    if (wt[n-1] > W):\n",
    "        return knapSack(W, wt, val, n-1)\n",
    "  \n",
    "    # return the maximum of two cases:\n",
    "    # (1) nth item included\n",
    "    # (2) not included\n",
    "    else:\n",
    "        return max(val[n-1] + knapSack(W-wt[n-1], wt, val, n-1),\n",
    "                   knapSack(W, wt, val, n-1))\n",
    "  \n",
    "# end of function knapSack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A Dynamic Programming based Python \n",
    "\n",
    "# Program for 0-1 Knapsack problem\n",
    "# Returns the maximum value that can \n",
    "# be put in a knapsack of capacity W\n",
    "def knapSack(W, wt, val, n):\n",
    "    K = [[0 for x in range(W + 1)] for x in range(n + 1)]\n",
    "  \n",
    "    # Build table K[][] in bottom up manner\n",
    "    for i in range(n + 1):\n",
    "        for w in range(W + 1):\n",
    "            if i == 0 or w == 0:\n",
    "                K[i][w] = 0\n",
    "            elif wt[i-1] <= w:\n",
    "                K[i][w] = max(val[i-1] + K[i-1][w-wt[i-1]],  K[i-1][w])\n",
    "            else:\n",
    "                K[i][w] = K[i-1][w]\n",
    "  \n",
    "    return K[n][W]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1e+03 ns, sys: 0 ns, total: 1e+03 ns\n",
      "Wall time: 3.1 µs\n",
      "\n",
      "The optimal solution is 1458\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "n = len(val)\n",
    "ans = knapSack(W, wt, val, n)\n",
    "print(f'\\nThe optimal solution is {ans}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First choice Hill Climbling, HC\n",
    "\n",
    "Generates successors until one is better than the current state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 0, 0, 0, 0, 1, 1, 1, 1]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Define translation from decimal to binary \n",
    "def dec_to_bin(dec_num, digits): \n",
    "    quotient = dec_num\n",
    "    bin_num = []\n",
    "\n",
    "    if dec_num == 1 | 0:\n",
    "        bin_num.append(dec_num)\n",
    "        while(len(bin_num)!= digits):\n",
    "            bin_num.append(0)\n",
    "        return bin_num\n",
    "    \n",
    "    while(quotient > 0): \n",
    "        reminders = dec_num % 2 \n",
    "        quotient = (dec_num - reminders) // 2 \n",
    "        dec_num = quotient \n",
    "        bin_num.append(reminders)\n",
    "    while(len(bin_num)!= digits):\n",
    "        bin_num.append(0)\n",
    "\n",
    "    bin_num = bin_num[::-1]\n",
    "    return bin_num\n",
    "\n",
    "dec_to_bin(783,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the fitness function that returns the total value of a given solution \n",
    "def fitness(solution): \n",
    "    # Initialize the variable\n",
    "    total_wt, total_val = 0, 0\n",
    "\n",
    "    # Iterate through the input string to calculate the weights & profits\n",
    "    for idx in range(len(solution)):\n",
    "        if solution[idx] == 1:\n",
    "            total_wt += wt[idx] # sum the weight\n",
    "            total_val += val[idx] # sum the value\n",
    "\n",
    "    # If either smaller or equal to weight restrain, return the total_val\n",
    "    if total_wt <= W: \n",
    "        return total_val \n",
    "    # Otherwise, return 0\n",
    "    return 0\n",
    "\n",
    "# Define the neighborhood function\n",
    "def get_neighbor(current_solution):\n",
    "    neighbor = current_solution.copy()\n",
    "    index = random.randint(0, len(current_solution)-1)\n",
    "    neighbor[index] = 1 - neighbor[index]\n",
    "    return neighbor\n",
    "\n",
    "def hill_climbing():\n",
    "    # Initialize the current solution\n",
    "    current_solution = [random.randint(0, 1) for _ in range(len(val))] \n",
    "    current_fitness = fitness(current_solution)\n",
    "\n",
    "    # Iterate until no better neighbor is found\n",
    "    while True:\n",
    "        # Get a neighbor solution\n",
    "        neighbor_solution = get_neighbor(current_solution)\n",
    "        neighbor_fitness = fitness(neighbor_solution)\n",
    "\n",
    "        # If the neighbor is better, update the current solution\n",
    "        if neighbor_fitness >= current_fitness:\n",
    "            current_solution = neighbor_solution\n",
    "            current_fitness = neighbor_fitness\n",
    "        # Otherwise, terminate and return the best solution found\n",
    "        else:\n",
    "            return current_solution, current_fitness\n",
    "        \n",
    "# Define the loop iterates apply Hill climbing (HC) algo.\n",
    "def HC_loop(iteration):\n",
    "    condition = True # Flag for while-loop\n",
    "    hc_results = [] # Store each iterateion result\n",
    "    idx, best_fitness = 0, 0 # Temporary variables\n",
    "    while condition:\n",
    "        # First iterative of HC \n",
    "        _ , current_fitness = hill_climbing()\n",
    "\n",
    "        # Find optimal solution, either equal or greater than the previous one\n",
    "        if current_fitness >= best_fitness:\n",
    "            hc_results.append(current_fitness)\n",
    "            best_fitness = current_fitness\n",
    "            idx += 1\n",
    "\n",
    "        # 500 iterations     \n",
    "        if idx == iteration:\n",
    "            condition = False\n",
    "            return hc_results, best_fitness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The optimal solution is 1458\n"
     ]
    }
   ],
   "source": [
    "# Parameters\n",
    "iterate = 500\n",
    "\n",
    "# Hill climbing algo. for 500 iterations, return the process in earch iteration & coverangce solution\n",
    "hc_results, best_fiteness = HC_loop(iterate)\n",
    "print(f'\\nThe optimal solution is {best_fiteness}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the result of every iterations\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.grid(color = 'gray', linestyle = '--', linewidth = 0.5)\n",
    "\n",
    "plt.xlabel('Iterations', size='16')\n",
    "plt.ylabel('Profits', size='16')\n",
    "plt.title('Hill climbing Algo',size='23')\n",
    "\n",
    "plt.plot(hc_results)\n",
    "plt.savefig('./01_knapsack_HC.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulated annealing, SA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the fitness function that returns the total value of a given solution\n",
    "def fitness(solution):\n",
    "    total_value,total_weight = 0,0\n",
    "    for i in range(len(solution)):\n",
    "        if solution[i] == 1:\n",
    "            total_value += val[i]\n",
    "            total_weight += wt[i]\n",
    "    if total_weight > W:\n",
    "        return 0\n",
    "    return total_value\n",
    "\n",
    "# Define the Simulated Annealing algorithm\n",
    "def simulated_annealing(max_iterations, initial_temperature, cooling_rate):\n",
    "    # Initialize the current solution randomly\n",
    "    current_solution = [random.randint(0, 1) for _ in range(len(val))]\n",
    "    current_fitness = fitness(current_solution)\n",
    "    best_solution = current_solution.copy()\n",
    "    best_fitness = current_fitness\n",
    "    \n",
    "    for i in range(max_iterations):\n",
    "        # Generate a neighbor solution by flipping a random bit\n",
    "        index = random.randint(0, len(val) - 1)\n",
    "        neighbor_solution = current_solution.copy()\n",
    "        neighbor_solution[index] = 1 - neighbor_solution[index]\n",
    "        neighbor_fitness = fitness(neighbor_solution)\n",
    "        \n",
    "        # Calculate the acceptance probability based on the current temperature\n",
    "        temperature = initial_temperature * math.pow(cooling_rate, i)\n",
    "        delta_fitness = neighbor_fitness - current_fitness\n",
    "        acceptance_probability = math.exp(delta_fitness / temperature)\n",
    "        \n",
    "        # If the neighbor solution is better or accepted, update the current solution\n",
    "        if neighbor_fitness > current_fitness or random.random() < acceptance_probability:\n",
    "            current_solution = neighbor_solution\n",
    "            current_fitness = neighbor_fitness\n",
    "        \n",
    "        # Update the best solution if necessary\n",
    "        if current_fitness > best_fitness:\n",
    "            best_solution = current_solution.copy()\n",
    "            best_fitness = current_fitness\n",
    "    \n",
    "    return best_solution, best_fitness\n",
    "\n",
    "# Define the loop iterates apply Simulated annealing (SA) algo.\n",
    "def SA_loop(iteration, max_iterations, initial_temperature, cooling_rate):\n",
    "\n",
    "    condition = True # Flag for while-loop\n",
    "    sa_results = [] # Store each iterateion result\n",
    "    idx, best_fitness = 0, 0 # Temporary variables\n",
    "\n",
    "    while condition:\n",
    "        # First iteration of HC \n",
    "        _ , current_fitness = simulated_annealing(max_iterations, initial_temperature, cooling_rate)\n",
    "\n",
    "        # Find optimonal solution, either equal or greater than the previous one\n",
    "        if current_fitness >= best_fitness:\n",
    "            sa_results.append(current_fitness)\n",
    "            best_fitness = current_fitness\n",
    "            idx += 1\n",
    "\n",
    "        # 500 iterations     \n",
    "        if idx == iteration:\n",
    "            condition = False\n",
    "            return sa_results, best_fitness\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The optimal solution is 1458\n"
     ]
    }
   ],
   "source": [
    "# Parameters\n",
    "max_iterations=100\n",
    "initial_temperature=1000\n",
    "cooling_rate=0.99\n",
    "iterate=500\n",
    "\n",
    "# Stimulated annealing algo. for 500 iterations, return the process in earch iteration & coverangce solution\n",
    "sa_results, best_fiteness = SA_loop(iterate,max_iterations, initial_temperature, cooling_rate)\n",
    "print(f'\\nThe optimal solution is {best_fiteness}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the result of every iterations\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.grid(color = 'gray', linestyle = '--', linewidth = 0.5)\n",
    "\n",
    "plt.xlabel('Iterations', size='16')\n",
    "plt.ylabel('Profits', size='16')\n",
    "plt.title('Simulated annealing (SA)',size='23')\n",
    "\n",
    "plt.plot(sa_results)\n",
    "plt.savefig('./output/01_knapsack_SA.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.grid(color = 'gray', linestyle = '--', linewidth = 0.5)\n",
    "\n",
    "plt.xlabel('Iterations', size='16')\n",
    "plt.ylabel('Profits', size='16')\n",
    "plt.title('0/1 knapsack problem',size='23')\n",
    "\n",
    "plt.plot(hc_results,'o-' ,label='Hill climbing algo')\n",
    "plt.plot(sa_results,'o-' ,label='Stimuated annealing algo')\n",
    "plt.legend(loc='lower right')\n",
    "plt.savefig('./output/01_knapsack_combine.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genetic algorithm, GA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "# Genetic Algorithm (GA) parameters\n",
    "population_size = 5000\n",
    "mutation_rate = 0.01\n",
    "crossover_rate = 0.8\n",
    "num_generations = 100\n",
    "\n",
    "# 0/1 Knapsack problem parameters\n",
    "val = [135, 139, 149, 150, 156, 163, 173, 184, 192, 201, 210, 214, 221, 229, 240]\n",
    "wt = [70, 73, 77, 80, 82, 87, 90, 94, 98, 106, 110, 113, 115, 118, 120]\n",
    "W = 750\n",
    "n = len(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1451\n",
      "Best solution: [1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1]\n",
      "Total value: 1451\n"
     ]
    }
   ],
   "source": [
    "# Initialize population randomly\n",
    "population = []\n",
    "for i in range(population_size):\n",
    "    chromosome = []\n",
    "    for j in range(n):\n",
    "        chromosome.append(random.randint(0, 1))\n",
    "    population.append(chromosome)\n",
    "\n",
    "# Define fitness function(target funciton)\n",
    "def fitness(chromosome):\n",
    "    total_val = 0\n",
    "    total_wt = 0\n",
    "    for i in range(n):\n",
    "        if chromosome[i] == 1:\n",
    "            total_val += val[i]\n",
    "            total_wt += wt[i]\n",
    "    if total_wt > W:\n",
    "        # Penalize solutions that exceed the weight constraint\n",
    "        return 0\n",
    "    else:\n",
    "        return total_val\n",
    "\n",
    "# GA main loop\n",
    "for generation in range(num_generations):\n",
    "    # Calculate fitness for each chromosome\n",
    "    fitnesses = [fitness(chromosome) for chromosome in population]\n",
    "\n",
    "    # Select parents using tournament selection\n",
    "    parents = []\n",
    "    for i in range(population_size):\n",
    "        tournament = random.sample(range(population_size), 2)\n",
    "        if fitnesses[tournament[0]] > fitnesses[tournament[1]]:\n",
    "            parents.append(population[tournament[0]])\n",
    "        else:\n",
    "            parents.append(population[tournament[1]])\n",
    "\n",
    "    # Crossover\n",
    "    offspring = []\n",
    "    for i in range(population_size):\n",
    "        if random.random() < crossover_rate:\n",
    "            parent1, parent2 = random.sample(parents, 2)\n",
    "            crossover_point = random.randint(1, n-1)\n",
    "            child = parent1[:crossover_point] + parent2[crossover_point:]\n",
    "        else:\n",
    "            child = random.choice(parents)\n",
    "        offspring.append(child)\n",
    "\n",
    "    # Mutation\n",
    "    for i in range(population_size):\n",
    "        for j in range(n):\n",
    "            if random.random() < mutation_rate:\n",
    "                offspring[i][j] = 1 - offspring[i][j]\n",
    "\n",
    "    # Replace the old population with the new offspring population\n",
    "    population = offspring\n",
    "\n",
    "# Find the best solution\n",
    "best_chromosome = None\n",
    "max_val = float('-inf')\n",
    "for chromosome in population:\n",
    "    total_val = 0\n",
    "    total_wt = 0\n",
    "    for i in range(n):\n",
    "        if chromosome[i] == 1:\n",
    "            total_val += val[i]\n",
    "            total_wt += wt[i]\n",
    "    if total_val > max_val and total_wt <= W:\n",
    "        print(total_val)\n",
    "        max_val = total_val\n",
    "        best_chromosome = chromosome\n",
    "\n",
    "print(\"Best solution:\", best_chromosome)\n",
    "print(\"Total value:\", max_val)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Particle Swarm optimization, PSO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "# Particle Swarm Optimization (PSO) parameters\n",
    "num_particles = 50\n",
    "max_iterations = 5000\n",
    "c1 = 2\n",
    "c2 = 2\n",
    "w = 0.7\n",
    "\n",
    "# 0/1 Knapsack problem parameters\n",
    "val = [135, 139, 149, 150, 156, 163, 173, 184, 192, 201, 210, 214, 221, 229, 240]\n",
    "wt = [70, 73, 77, 80, 82, 87, 90, 94, 98, 106, 110, 113, 115, 118, 120]\n",
    "W = 750\n",
    "n = len(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best solution found:\n",
      "[1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1]\n",
      "Fitness = 1447\n"
     ]
    }
   ],
   "source": [
    "# Initialize particles with random binary strings\n",
    "particles = []\n",
    "for i in range(num_particles):\n",
    "    particle = []\n",
    "    for j in range(n):\n",
    "        particle.append(random.randint(0, 1))\n",
    "    particles.append(particle)\n",
    "\n",
    "# Define fitness function\n",
    "def fitness(particle):\n",
    "    total_val = 0\n",
    "    total_wt = 0\n",
    "    for i in range(n):\n",
    "        if particle[i] == 1:\n",
    "            total_val += val[i]\n",
    "            total_wt += wt[i]\n",
    "    if total_wt > W:\n",
    "        total_val = 0\n",
    "    return total_val\n",
    "\n",
    "# Initialize personal and global bests\n",
    "pbests = particles.copy()\n",
    "gbest = particles[0].copy()\n",
    "for particle in particles:\n",
    "    if fitness(particle) > fitness(gbest):\n",
    "        gbest = particle.copy()\n",
    "db =[]\n",
    "# PSO main loop\n",
    "for iteration in range(max_iterations):\n",
    "    for i in range(num_particles):\n",
    "        # Update velocity\n",
    "        for j in range(n):\n",
    "            r1 = random.random()\n",
    "            r2 = random.random()\n",
    "            particles[i][j] = int(round(particles[i][j] + w * particles[i][j] +\n",
    "                                         c1 * r1 * (pbests[i][j] - particles[i][j]) +\n",
    "                                         c2 * r2 * (gbest[j] - particles[i][j])))\n",
    "        # Update personal best\n",
    "        if fitness(particles[i]) > fitness(pbests[i]):\n",
    "            pbests[i] = particles[i].copy()\n",
    "\n",
    "        # Update global best\n",
    "        if fitness(pbests[i]) > fitness(gbest):\n",
    "            gbest = pbests[i].copy()\n",
    "        \n",
    "        db.append(fitness(gbest))\n",
    "\n",
    "# Print the best solution found\n",
    "print(\"Best solution found:\")\n",
    "print(gbest)\n",
    "print(\"Fitness = {}\".format(fitness(gbest)))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ant colony optimization, ACO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best solution: [1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1]\n",
      "Total value: 559\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "# Ant Colony Optimization (ACO) parameters\n",
    "num_ants = 50\n",
    "max_iterations = 10000\n",
    "alpha = 1.0\n",
    "beta = 3.0\n",
    "rho = 0.1\n",
    "q0 = 0.9\n",
    "\n",
    "# 0/1 Knapsack problem parameters\n",
    "val = [135, 139, 149, 150, 156, 163, 173, 184, 192, 201, 210, 214, 221, 229, 240]\n",
    "wt = [70, 73, 77, 80, 82, 87, 90, 94, 98, 106, 110, 113, 115, 118, 120]\n",
    "W = 750\n",
    "n = len(val)\n",
    "\n",
    "# Initialize pheromone trail with 1.0\n",
    "tau = [1.0 for j in range(n)]\n",
    "\n",
    "# Define probability function\n",
    "def probability(ant, item):\n",
    "    total_val = 0\n",
    "    total_wt = 0\n",
    "    for i in range(n):\n",
    "        if ant[i] == 1:\n",
    "            total_val += val[i]\n",
    "            total_wt += wt[i]\n",
    "    if total_wt + wt[item] > W:\n",
    "        return 0.0\n",
    "    else:\n",
    "        return (tau[item] ** alpha) * ((1.0 / wt[item]) ** beta)\n",
    "\n",
    "# ACO main loop\n",
    "for iteration in range(max_iterations):\n",
    "    # Initialize ants with empty knapsacks\n",
    "    ants = [[0 for j in range(n)] for i in range(num_ants)]\n",
    "\n",
    "    # Construct solutions using the probability function\n",
    "    for i in range(num_ants):\n",
    "        for j in range(n):\n",
    "            p = random.random()\n",
    "            if p < q0:\n",
    "                # Exploit: choose the item with the highest probability\n",
    "                item = max(range(n), key=lambda x: probability(ants[i], x))\n",
    "            else:\n",
    "                # Explore: choose an item randomly with probability proportional to its probability\n",
    "                probs = [probability(ants[i], x) for x in range(n)]\n",
    "                cum_probs = [sum(probs[:x+1]) for x in range(n)]\n",
    "                item = next((x for x, p in enumerate(cum_probs) if p >= random.random()), n-1)\n",
    "\n",
    "            ants[i][item] = 1\n",
    "\n",
    "    # Update pheromone trail\n",
    "    for i in range(n):\n",
    "        tau[i] = (1.0 - rho) * tau[i]\n",
    "        for j in range(num_ants):\n",
    "            total_val = 0\n",
    "            for k in range(n):\n",
    "                if ants[j][k] == 1:\n",
    "                    total_val += val[k]\n",
    "            if ants[j][i] == 1:\n",
    "                tau[i] += rho * (total_val / n)\n",
    "\n",
    "# Find the best solution\n",
    "best_ant = None\n",
    "max_val = float('-inf')\n",
    "for i in range(num_ants):\n",
    "    total_val = 0\n",
    "    total_wt = 0\n",
    "    for j in range(n):\n",
    "        if ants[i][j] == 1:\n",
    "            total_val += val[j]\n",
    "            total_wt += wt[j]\n",
    "    if total_val > max_val and total_wt <= W:\n",
    "        max_val = total_val\n",
    "        best_ant = ants[i]\n",
    "\n",
    "print(\"Best solution:\", best_ant)\n",
    "print(\"Total value:\", max_val)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gray wolf optimization, GWO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "\n",
    "# Grey Wolf Optimizer (GWO) parameters\n",
    "num_wolves = 5\n",
    "max_iterations = 100\n",
    "a = 2  # alpha\n",
    "b = 3  # beta\n",
    "d = 0.5  # delta\n",
    "\n",
    "# 0/1 Knapsack problem parameters\n",
    "val = [135, 139, 149, 150, 156, 163, 173, 184, 192, 201, 210, 214, 221, 229, 240]\n",
    "wt = [70, 73, 77, 80, 82, 87, 90, 94, 98, 106, 110, 113, 115, 118, 120]\n",
    "W = 750\n",
    "n = len(val)\n",
    "\n",
    "# Initialize wolf positions randomly\n",
    "wolves = np.zeros((num_wolves, n))\n",
    "for i in range(num_wolves):\n",
    "    for j in range(n):\n",
    "        wolves[i][j] = random.randint(0, 1)\n",
    "\n",
    "# Define fitness function\n",
    "def fitness(wolf):\n",
    "    total_val = 0\n",
    "    total_wt = 0\n",
    "    for i in range(n):\n",
    "        if wolf[i] == 1:\n",
    "            total_val += val[i]\n",
    "            total_wt += wt[i]\n",
    "    if total_wt > W:\n",
    "        # Penalize solutions that exceed the weight constraint\n",
    "        return 0\n",
    "    else:\n",
    "        return total_val\n",
    "\n",
    "# GWO main loop\n",
    "for iteration in range(max_iterations):\n",
    "    # Calculate fitness for each wolf\n",
    "    fitnesses = np.array([fitness(wolf) for wolf in wolves])\n",
    "\n",
    "    # Update alpha, beta, and delta wolves\n",
    "    alpha_wolf = wolves[np.argmax(fitnesses)]\n",
    "    beta_wolf = wolves[np.argsort(fitnesses)[-2]]\n",
    "    delta_wolf = np.zeros(n)\n",
    "    for j in range(n):\n",
    "        delta_wolf[j] = alpha_wolf[j] - d * abs(beta_wolf[j] - alpha_wolf[j])\n",
    "\n",
    "    # Update positions of all wolves\n",
    "    for i in range(num_wolves):\n",
    "        for j in range(n):\n",
    "            r1 = random.random()  # random number between 0 and 1\n",
    "            r2 = random.random()  # random number between 0 and 1\n",
    "            A = 2 * a * r1 - a\n",
    "            C = 2 * r2\n",
    "            D = abs(C * delta_wolf[j] - wolves[i][j])\n",
    "            new_pos = np.clip(alpha_wolf[j] - A * D, 0, 1)\n",
    "            wolves[i][j] = new_pos\n",
    "\n",
    "# Find the best solution\n",
    "best_wolf = None\n",
    "max_val = float('-inf')\n",
    "for i in range(num_wolves):\n",
    "    total_val = 0\n",
    "    total_wt = 0\n",
    "    for j in range(n):\n",
    "        if wolves[i][j] == 1:\n",
    "            total_val += val[j]\n",
    "            total_wt += wt[j]\n",
    "    if total_val > max_val and total_wt <= W:\n",
    "        max_val = total_val\n",
    "        best_wolf = wolves[i]\n",
    "\n",
    "print(\"Best solution:\", best_wolf)\n",
    "print(\"Total value:\", max_val)\n"
   ]
  }
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