  Optimization aims at selecting a feasible set of parameters in an attempt to
solve a particular problem, being applied in a wide range of applications, such
as operations research, machine learning fine-tuning, and control engineering,
among others. Nevertheless, traditional iterative optimization methods use the
evaluation of gradients and Hessians to find their solutions, not being
practical due to their computational burden and when working with non-convex
functions. Recent biological-inspired methods, known as meta-heuristics, have
arisen in an attempt to fulfill these problems. Even though they do not
guarantee to find optimal solutions, they usually find a suitable solution. In
this paper, we proposed a Python-based meta-heuristic optimization framework
denoted as Opytimizer. Several methods and classes are implemented to provide a
user-friendly workspace among diverse meta-heuristics, ranging from
evolutionary- to swarm-based techniques.
