This repository provides the MATLAB implementation of generating a benchmark suite of sequential transfer optimization problems (STOPs). To instantiate an STOP, we need to set up the following six parameters: the target family, the transfer scenario, the optimum optimum, the similarity distribution, the task dimension, and the number of source tasks. Their available realizations are as follows:
- Task families (F):
Sphere,Ellipsoid,Schwefel,Quartic,Ackley,Rastrigin,Griewank, andLevy. - Transfer scenarios (T):
TaandTe. - Optimum coverages (xi): 0, 0.7, 1.
- Similarity distributions (p):
c,u,i,d. - Task dimensions (d): positive integers.
- Numbers of source tasks (k): positive integers.
In this work, we name an STOP as F-T-xi-p-d-k, where F denotes the target family, T represents the transfer scenario, xi is the optimum coverage, p represents the similarity distribution, d denotes the task dimension, k is the number of source tasks. According to this naming rule, we specify the following 12 STOPs to form the benchmark suite,
| Problem ID | Problem Specification |
|---|---|
| STOP 1 | Sphere-Ta-xi0-pc-50-k |
| STOP 2 | Ellipsoid-Te-xi0-pu-25-k |
| STOP 3 | Schwefel-Ta-xi0-pi-30-k |
| STOP 4 | Quartic-Te-xi0-pd-50-k |
| STOP 5 | Ackley-Ta-xi1-pi-25-k |
| STOP 6 | Rastrigin-Te-xi1-pu-50-k |
| STOP 7 | Griewank-Ta-xi0.7-pi-25-k |
| STOP 8 | Levy-Te-xi1-pd-30-k |
| STOP 9 | Sphere-Ta-xi1-pc-25-k |
| STOP 10 | Rastrigin-Te-xi0.7-pc-30-k |
| STOP 11 | Ackley-Ta-xi0.7-pc-50-k |
| STOP 12 | Ellipsoid-Te-xi1-pc-50-k |
In this repository, we employ evolutionary algorithm (EA) to denmonstrate the generation process of the 12 STOPs, whose scripts can be found at STOP-EA.
If you find this repo useful for your research, please consider to cite:
@article{xue2023scalable,
title={A Scalable Test Problem Generator for Sequential Transfer Optimization},
author={Xue, Xiaoming and Yang, Cuie and Feng, Liang and Zhang, Kai and Song, Linqi and Tan, Kay Chen},
journal={arXiv preprint arXiv:2304.08503},
year={2023}
}...