The sparrow search algorithm is a heuristic random search algorithm
It works like this:
- Randomly select a set of candidate solutions from the search space.
- Evaluate the set of candidate solutions, and save the optimal solution.
- A set of candidate solutions is again randomly selected from the search space.
- Evaluate the new candidate solution, and update the optimal solution if it is better than the current optimal solution.
- Repeat steps 3 and 4 until a certain stopping condition is met, such as reaching the maximum number of iterations.
The algorithm is named "sparrow search" because it works similarly to the way sparrows search for food in fields. That is to search randomly but non-stop, and keep saving the best food found.
This heuristic random search algorithm is suitable for optimization problems with complex solution spaces and discontinuous objective functions. Its advantages are simple and universal, but its disadvantage is that it does not use the problem structure information and the efficiency is not high.
In general, the sparrow search algorithm is a global search algorithm based on randomness, which is used to deal with complex optimization problems that are difficult to solve using other algorithms.
python code
The following is the Python implementation of the sparrow search algorithm:
import random
def sparrow_search(function, bounds, n_iterations):
best = None