The probabilistic niching evolutionary computation framework is proposed to solve multi-modal optimization problems (MMOP) using non-visiting mechanism. An enhanced binary space partition tree is built to structurally organize the space visiting information. Based on the tree, a probabilistic niching strategy is defined to reinforce exploration and exploitation by making full use of the structural historical information. The proposed framework is universal for incorporating various baseline MMOP optimizers.
$ make
Two executable files named PNF_DE and PNF_PSO are available in the current directory.
$ ./run.sh
Run the codes in the terminal.
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For more information please refer to our paper
If the code or paper is useful for your work, please cite our paper
with plain text
T. Huang, Y. -J. Gong, W. -N. Chen, H. Wang and J. Zhang, "A Probabilistic Niching Evolutionary Computation Framework Based on Binary Space Partitioning," in IEEE Transactions on Cybernetics, vol. 52, no. 1, pp. 51-64, Jan. 2022, doi: 10.1109/TCYB.2020.2972907.
or bibtex
@ARTICLE{9032378, author={Huang, Ting and Gong, Yue-Jiao and Chen, Wei-Neng and Wang, Hua and Zhang, Jun}, journal={IEEE Transactions on Cybernetics}, title={A Probabilistic Niching Evolutionary Computation Framework Based on Binary Space Partitioning}, year={2022}, volume={52}, number={1}, pages={51-64}, doi={10.1109/TCYB.2020.2972907}}