A data-driven evolutionary transfer optimization for expensive problems in dynamic environments

A data-driven evolutionary transfer optimization for expensive problems in dynamic environments Ke Li*, Renzhi Chen*, Xin Yao* [Paper] [Supplementary]

Overview

This repository contains Python implementation of the algorithm framework for Batched Data-Driven Evolutionary Multi-Objective Optimization Based on Manifold Interpolation.

Code Structure

algorithms/ --- algorithms definitions

problems/ --- multi-objective problem definitions

revision/ -- patch for Gpy package

scripts/ --- scripts for batch experiments

├── build.sh --- complie the c lib for test problems

├── run.sh -- run the experiment

main.py --- main execution file

Requirements

• Python version: tested in Python 3.7.7
• Operating system: tested in Ubuntu 20.04

Getting Started

Basic usage

Run the main file with python with specified arguments:

python3.7 main.py --problem Movingpeak --n-var 6 

Parallel experiment

Run the script file with bash, for example:

./run.sh

Result

The optimization results are saved in txt format. They are stored under the folder:

output/data/{problem}/x{n}y{m}/{algo}-{exp-name}/{seed}/

Citation

If you find our repository helpful to your research, please cite our paper:

@article{KeLi2023,
  author={Li, Ke and Chen, Renzhi and Yao, Xin},
  journal={IEEE Transactions on Evolutionary Computation}, 
  title={A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments}, 
  year={2023},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TEVC.2023.3307244}}