Optimization problems from different fields are generally difficult to solve due to the large solution space

Optimization problems from different fields are generally difficult to solve due to the large solution space. These problems are present in our daily life and range from determining the best delivery plan for a distribution company to finding genes associated with specific diseases, where an exhaustive search would take longer than the universe has existed. Although there are several optimization algorithms, different meta-heuristics inspired by nature have been proposed to achieve improved results. These nature-based algorithms attempt to mimic the behavior of ant colonies, bees, bats, wolves, and even black holes, among others and have produced remarkably solutions for different problems. In this talk, I will present a brief overview of these nature-based algorithms and some successful applications.