Nature-inspired algorithms are a very popular tool for solving optimization problems. Since the beginning of their era, numerous variants of nature-inspired algorithms were developed. To prove their versatility, those were tested in various domains on various applications, especially when they are hybridized, modified or adapted. However, implementation of nature-inspired algorithms is sometimes difficult, complex and tedious task. In order to break this wall, NiaPy is intended for simple and quick use, without spending a time for implementing algorithms from scratch.
Our mission is to build a collection of nature-inspired algorithms and create a simple interface for managing the optimization process along with statistical evaluation. NiaPy will offer:
- numerous benchmark functions implementations,
- use of various nature-inspired algorithms without struggle and effort with a simple interface and
- easy comparison between nature-inspired algorithms
This framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!