Simulation of Ant Colony Optimization Gaming Project in C++

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3 months ago

#include <iostream>
#include <vector>
#include <cstdlib>
#include <ctime>
#include <algorithm>

using namespace std;

// Grid dimensions
const int WIDTH = 10;
const int HEIGHT = 10;

// Directions for movement (right, down, left, up)
const int dx[] = {0, 1, 0, -1};
const int dy[] = {1, 0, -1, 0};

// Ant class
class Ant {
public:
    int x, y;
    Ant(int startX, int startY) : x(startX), y(startY) {}
    void move() {
        int direction = rand() % 4;
        x += dx[direction];
        y += dy[direction];
        x = max(0, min(WIDTH - 1, x));
        y = max(0, min(HEIGHT - 1, y));
    }
};

// Main function
int main() {
    srand(static_cast<unsigned>(time(0))); // Seed for random number generation

    vector<Ant> ants;
    int numAnts = 10;
    int startX = 0, startY = 0;
    int goalX = WIDTH - 1, goalY = HEIGHT - 1;

    // Initialize ants
    for (int i = 0; i < numAnts; ++i) {
        ants.emplace_back(startX, startY);
    }

    // Simulation loop
    for (int step = 0; step < 100; ++step) {
        cout << "Step " << step + 1 << ":" << endl;
        for (auto& ant : ants) {
            ant.move();
            cout << "Ant at (" << ant.x << ", " << ant.y << ")" << endl;
        }

        // Check if any ant reached the goal
        bool goalReached = false;
        for (const auto& ant : ants) {
            if (ant.x == goalX && ant.y == goalY) {
                goalReached = true;
                break;
            }
        }

        if (goalReached) {
            cout << "An ant has reached the goal!" << endl;
            break;
        }

        cout << endl;
    }

    return 0;
}

#include <iostream>

#include <vector>

#include <cstdlib>

#include <ctime>

#include <algorithm>

using namespace std;

// Grid dimensions

const int WIDTH = 10;

const int HEIGHT = 10;

// Directions for movement (right, down, left, up)

const int dx[] = {0, 1, 0, -1};

const int dy[] = {1, 0, -1, 0};

// Ant class

class Ant {

public:

int x, y;

Ant(int startX, int startY) : x(startX), y(startY) {}

void move() {

int direction = rand() % 4;

x += dx[direction];

y += dy[direction];

x = max(0, min(WIDTH - 1, x));

y = max(0, min(HEIGHT - 1, y));

}

};

// Main function

int main() {

srand(static_cast<unsigned>(time(0))); // Seed for random number generation

vector<Ant> ants;

int numAnts = 10;

int startX = 0, startY = 0;

int goalX = WIDTH - 1, goalY = HEIGHT - 1;

// Initialize ants

for (int i = 0; i < numAnts; ++i) {

ants.emplace_back(startX, startY);

}

// Simulation loop

for (int step = 0; step < 100; ++step) {

cout << "Step " << step + 1 << ":" << endl;

for (auto& ant : ants) {

ant.move();

cout << "Ant at (" << ant.x << ", " << ant.y << ")" << endl;

}

// Check if any ant reached the goal

bool goalReached = false;

for (const auto& ant : ants) {

if (ant.x == goalX && ant.y == goalY) {

goalReached = true;

break;

}

if (goalReached) {

cout << "An ant has reached the goal!" << endl;

break;

}

cout << endl;

}

return 0;

}

Explanation

Headers:
- <iostream>: For input and output operations.
- <vector>: For using the std::vector container to store ants.
- <cstdlib>: For rand() and srand() functions.
- <ctime>: For time() function to seed the random number generator.
- <algorithm>: For the max and min functions.
Constants:
- WIDTH and HEIGHT: Dimensions of the grid.
- dx and dy: Arrays representing possible movement directions (right, down, left, up).
Ant Class:
- Attributes:
  - x and y: Current position of the ant.
- Methods:
  - move(): Moves the ant in a random direction and ensures it stays within grid boundaries.
Main Function:
- Initialization:
  - Seeds the random number generator.
  - Creates a vector of ants and initializes their starting position.
- Simulation Loop:
  - Runs for a fixed number of steps (100 in this case).
  - Each ant moves randomly in one of the four directions.
  - Prints the position of each ant after each step.
  - Checks if any ant has reached the goal position (goalX, goalY).
  - If the goal is reached, the simulation ends early.

Notes:

Random Movement: The ants move randomly, which is a basic approach. In a more advanced ACO simulation, ants would deposit pheromones, and their movement would be influenced by pheromone levels.

Grid Boundaries: The movement is constrained within the grid boundaries using max and min functions.
Simplification: This program provides a basic framework. Real ACO algorithms involve pheromone trails and probabilistic decision-making for more complex optimization tasks.

Explanation

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