Abstract: Bio-inspired algorithms are based on the collective behavior of interacting organisms and are used to solve or to approach efficient solutions for large optimization problems. This article evaluates a new parallel library of a family of bio-inspired algorithms. The library offers the implementation of some basic algorithms, being easily extensible through interfaces, and explores the parallelism using SIMD-type instructions. Evaluations of performances are presented using seven test functions applied to each of the implemented algorithms. The tests also allowed to show that parallel implementations offer higher performances in all cases, reaching up to 20 times for some functions.