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 # PyPop7: a Pure-PYthon open-source library of POPulation-based (evolution / swarm / pattern search) black-box OPtimization
 
-[![Python](https://img.shields.io/badge/Python-3-yellow.svg)](https://www.python.org/) [![GNU General Public License v3.0](https://img.shields.io/badge/license-GNU%20GPL--v3.0-green.svg)](https://github.com/Evolutionary-Intelligence/pypop/blob/main/LICENSE) [![PyPI for PyPop7](https://img.shields.io/badge/PyPI-pypop7-yellowgreen.svg)](https://pypi.org/project/pypop7/) [![Documentation Status](https://readthedocs.org/projects/pypop/badge/?version=latest)](https://pypop.readthedocs.io/en/latest/?badge=latest) [![arxiv](https://img.shields.io/badge/arxiv-2212.05652-red)](https://arxiv.org/abs/2212.05652) [![Downloads](https://static.pepy.tech/badge/pypop7)](https://pepy.tech/project/pypop7) [![Downloads](https://static.pepy.tech/badge/pypop7/month)](https://pepy.tech/project/pypop7)
+[![Python](https://img.shields.io/badge/Python-3-yellow.svg)](https://www.python.org/) [![GNU General Public License v3.0](https://img.shields.io/badge/license-GNU%20GPL--v3.0-green.svg)](https://github.com/Evolutionary-Intelligence/pypop/blob/main/LICENSE) [![PyPI for PyPop7](https://img.shields.io/badge/PyPI-pypop7-yellowgreen.svg)](https://pypi.org/project/pypop7/) [![Documentation Status](https://readthedocs.org/projects/pypop/badge/?version=latest)](https://pypop.readthedocs.io/en/latest/?badge=latest) [![arxiv](https://img.shields.io/badge/arxiv-2212.05652-red)](https://arxiv.org/abs/2212.05652) [![Downloads](https://static.pepy.tech/badge/pypop7)](https://pepy.tech/project/pypop7) [![Downloads](https://static.pepy.tech/badge/pypop7/month)](https://pepy.tech/project/pypop7) [![Static Badge](https://img.shields.io/badge/WeChat-green)](docs/logo/WeChatGroupTo20240710.jpg)
 
 ```PyPop7``` is a *Pure-PYthon* open-source library of **POPulation-based OPtimization** for single-objective, real-parameter, black-box problems (*currently actively maintained*). Its goal is to provide a *unified* interface and a set of *elegant* algorithmic implementations (e.g., evolutionary algorithms, swarm-based optimizers, pattern search, etc.) for **Black-Box Optimization (BBO)**, *particularly population-based optimizers*, in order to facilitate research repeatability, benchmarking of BBO, and especially real-world applications.