https://github.com/eastonarcher/python-simulated-annealing

⚡Solve the Eight Queens Puzzle with Simulated Annealing for optimal queen placement on a chessboard
https://github.com/eastonarcher/python-simulated-annealing

chess python3 queens-problem simulated-annealing simulated-annealing-algorithm

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⚡Solve the Eight Queens Puzzle with Simulated Annealing for optimal queen placement on a chessboard

• Host: GitHub
• URL: https://github.com/eastonarcher/python-simulated-annealing
• Owner: EastonArcher
• Created: 2024-02-08T04:13:03.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-05-10T18:21:20.000Z (8 months ago)
• Last Synced: 2024-05-10T19:32:50.479Z (8 months ago)
• Topics: chess, python3, queens-problem, simulated-annealing, simulated-annealing-algorithm
• Language: Python
• Homepage:
• Size: 70.3 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
 # 8 Queens Problem ♟

This code implements the Simulated Annealing algorithm to solve the Eight Queens Puzzle. The puzzle involves placing eight chess queens on an 8×8 chessboard such that no two queens threaten each other; they cannot share the same row, column, or diagonal.

## Simulated Annealing

Simulated Annealing is a probabilistic optimization algorithm inspired by the process of annealing in metallurgy. It starts with an initial solution and iteratively explores neighboring solutions, gradually decreasing the probability of accepting worse solutions as it progresses. This allows the algorithm to escape local optima and converge towards a global optimum.

## 'Board'

- Initializes and represents the chessboard.

* ```reset()```: Resets the board with random queen positions.

* ```calculateCost()```: Calculates the number of queen conflicts (threats) on the board.

* ```calculateCostWithQueens(queens)```: Static method to calculate the number of conflicts given a set of queen positions.

* ```toString(queens)```: Static method to convert queen positions to a string representation.

  

## 'SimulatedAnnealing'

- Initializes and runs the Simulated Annealing algorithm.

* ```run()```: Runs the Simulated Annealing loop to find a solution.

* ```getElapsedTime()```: Calculates and returns the elapsed time during execution.

## Running the Code

To run the code:

```

python simulated-annealing.py

```

The initial board state and the solution (if it is found) will be printed along with the elapsed time.







***(School Project)***