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Multi-learning rate optimization spiking neural P systems for solving the discrete optimization problems

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Abstract

To further improve the performance of optimization spiking neural P system (OSNPS), a multi-learning rate optimization spiking neural P system (MLOSNPS) is proposed. More specifically, by borrowing the distributed population structure of DAOSNPS, the distributed population structure with multiple subpopulations, single migration individual and information exchange considering convergence and diversity is adopted in MLOSNPS. In addition, three different learning rates in OSNPS, AOSNPS and DAOSNPS are used at different evolutionary stages in MLOSNPS. The experimental results in 0/1 knapsack problems show that MLOSNPS achieves a better balance between exploration and exploitation than OSNPS, AOSNPS and DAOSNPS.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61972324, 61672437, 61702428), the Sichuan Science and Technology Program (2021YFS0313, 2021YFG0133), Beijing Advanced Innovation Center for Intelligent Robots and Systems (2019IRS14).

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  1. Jianping Dong, Gexiang Zhang, Biao Luo and Dongyang Xiao contributed equally to this work.

Authors and Affiliations

  1. School of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610059, China

    Jianping Dong

  2. Research Center for Artificial Intelligence, Chengdu University of Technology, Chengdu, 610059, China

    Gexiang Zhang, Biao Luo & Dongyang Xiao

  3. School of Automation, Chengdu University of Information Technology, Chengdu, 610225, China

    Gexiang Zhang

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  1. Jianping Dong
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Correspondence to Gexiang Zhang.

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Dong, J., Zhang, G., Luo, B. et al. Multi-learning rate optimization spiking neural P systems for solving the discrete optimization problems. J Membr Comput 4, 209–221 (2022). https://doi.org/10.1007/s41965-022-00105-6

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