The DEAP library (a Python library for EAs) contains some benchmarks. In particular, you may want to look at the following functions
Besides the DEAP library repository, you might want to look at several well established MOEA frameworks such as PyMoo (Python) and PlatEMO (MatLab). Both have implementations of well known MOEAs and benchmark functions. You can look through their collection of benchmark functions for inspiration, and also implement your algorithm with those framework so that you can easily test your method's performance on their benchmark functions.
PlatEMO even has a GUI for experimental study, where you can choose the algorithms and the benchmark functions to test, followed by a Wilcoxon’s rank sum test to see how your algorithm really perform compared with other algorithms. For PyMoo you can create a new test file by following the existing examples, but, as far as I know, it doesn't have an experimental study platform similar to PlatEMO yet.
You may also be interested in the paper Scalable and Customizable Benchmark Problems for Many-Objective Optimization (2020).