Nature-inspired algorithms are really powerful and are commonly used for solving NP-hard problems (eg. traveling salesman problem) or other computationally expensive tasks. They are also called optimization algorithms. Nature-inspired algorithms are trying to find the best solution for the problem, however, it is not guaranteed the best solution will be found.

Hyperparameter tuning is usually done using the grid search or random search. The problem of the grid search is that it is really expensive since it tries all of the possible parameter combinations. Random search will try a certain number of random parameter combinations. It is unlikely that it will find the best combination of parameters, however, it is much faster than grid search.

The echolocation of bats is the fundamental part of the Bat Algorithm (Xin-She Yang 2010).

And here comes the nature-inspired algorithms. They are faster than grid search and comes with the possibility of finding the best solution — the best combination of hyperparameters. However, the result will vary from algorithm to algorithm (there are many nature-inspired algorithms, for example: Bat Algorithm, Firefly Algorithm…), also these algorithms have their own parameters that control the way how they search for the solution. If you know how these algorithms work, you might want to set these parameters to improve the search process.

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