Example_Code_for_Simulated_Annealing.ipynb

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      "source": [
        "import numpy as np\n",
        "\n",
        "def objective_function(x):\n",
        "    return x[0]**2 + x[1]**2 + 3 * np.sin(2 * x[0]) * np.cos(2 * x[1])\n",
        "\n",
        "def simulated_annealing(objective, bounds, n_iterations, temp):\n",
        "    solution = bounds[:, 0] + (bounds[:, 1] - bounds[:, 0]) * np.random.rand(2)\n",
        "    solution_eval = objective(solution)\n",
        "    best = solution\n",
        "    best_eval = solution_eval\n",
        "\n",
        "    for i in range(n_iterations):\n",
        "        candidate = solution + np.random.normal(0, 1, size=2)\n",
        "        candidate_eval = objective(candidate)\n",
        "\n",
        "        if candidate_eval < solution_eval or np.exp((solution_eval - candidate_eval) / temp) > np.random.rand():\n",
        "            solution, solution_eval = candidate, candidate_eval\n",
        "            if candidate_eval < best_eval:\n",
        "                best, best_eval = candidate, candidate_eval\n",
        "\n",
        "        temp *= 0.9  # Temperature decay\n",
        "    return best, best_eval\n",
        "\n",
        "bounds = np.array([[-5.0, 5.0], [-5.0, 5.0]])\n",
        "best_solution, best_value = simulated_annealing(objective_function, bounds, 1000, temp=10)\n",
        "print(f\"Best Solution: {best_solution}, Best Value: {best_value:.4f}\")"
      ]
    }
  ]
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