searches.simulated_annealing

Functions

simulated_annealing(→ Any)	Implementation of the simulated annealing algorithm. We start with a given state,
test_f1(x, y)

Module Contents

searches.simulated_annealing.simulated_annealing(search_prob, find_max: bool = True, max_x: float = math.inf, min_x: float = -math.inf, max_y: float = math.inf, min_y: float = -math.inf, visualization: bool = False, start_temperate: float = 100, rate_of_decrease: float = 0.01, threshold_temp: float = 1) → Any

Implementation of the simulated annealing algorithm. We start with a given state, find all its neighbors. Pick a random neighbor, if that neighbor improves the solution, we move in that direction, if that neighbor does not improve the solution, we generate a random real number between 0 and 1, if the number is within a certain range (calculated using temperature) we move in that direction, else we pick another neighbor randomly and repeat the process.

Args:

search_prob: The search state at the start. find_max: If True, the algorithm should find the minimum else the minimum. max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y. visualization: If True, a matplotlib graph is displayed. start_temperate: the initial temperate of the system when the program starts. rate_of_decrease: the rate at which the temperate decreases in each iteration. threshold_temp: the threshold temperature below which we end the search

Returns a search state having the maximum (or minimum) score.

searches.simulated_annealing.test_f1(x, y)