Accurate Peak Detection in Multimodal Optimization via Approximated Landscape Learning

Here we provide sourcecodes of APDMMO, which is accepted by GECCO 2025.

Citation

The PDF version of the paper is available here. If you find our APDMMO useful, please cite it in your publications or projects.

@article{ma2025accurate,
  title={Accurate Peak Detection in Multimodal Optimization via Approximated Landscape Learning},
  author={Ma, Zeyuan and Lian, Hongqiao and Qiu, Wenjie and Gong, Yue-Jiao},
  journal={arXiv preprint arXiv:2503.18066},
  year={2025}
}

Requirements

You can install all of dependencies of APDMMO via the command below.

pip install -r requirements.txt

Run the code

The optimization process on CEC2013 multimodal optimization benchmark can be activated via the command below.

python run.py

Results

The optimization process includes three stages:

1. Global Landscape Fitting(GLF): This stage is used to train a Landscape Learner for a problem. The Log files will be saved to ./log, and the trained models will be saved to ./checkpoint, while the information of mu and std for normalization are stored in ./mu_std_info. The contour maps of 1D/2D problems are saved in ./pic.

2. Free-of-trial Peak Detection(FPD): This stage is used to detect potential peak areas. The optimized solutions are saved in ./optimization_result.

3. Parallel Local Search(PLS): The last stage is designed to perform local optimization. The results of Peak Ratio(PR) and Success Rate(SR) are saved to ./result.