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Migration strategy in distributed adaptive optimization spiking neural P systems

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Abstract

The distributed adaptive optimization spiking neural P systems (DAOSNPS) are membrane optimization algorithms which does not depend on evolutionary algorithms or swarm intelligence algorithms. It is acknowledged that population structure is very important to enhance the performance of the original optimization algorithms. In this paper, a proper migration strategy including dynamic migration interval is proposed to boost the performance of DAOSNPS. More specifically, first of all, the dynamic migration interval is introduced to speed up the convergence. Furthermore, the number of subpopulations and the migration size are discussed to select the appropriate number of subpopulations and the migration size to balance the exploration and exploitation ability. Finally, two strategies of migration individual selection and replacement individual selection are analyzed and compared in DAOSNPS. The extensive experimental results on the 0/1 knapsack problem show that DAOSNPS with the dynamic migration interval can effectively decrease time consumption. The appropriate migration strategy can obtain better balance in terms of the quality of convergence and diversity.

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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, 2021YJ0086, 2022YFG0181, 23NSFTD0049, 23ZDYF0247), and Chengdu Technological Innovation Development Project (2022-YF05-01134-SN).

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Author notes

  1. Jianping Dong, Gexiang Zhang, Dongyang Xiao, Biao Luo and Haina Rong have 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, Dongyang Xiao & Biao Luo

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

    Gexiang Zhang

  4. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 611756, China

    Haina Rong

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  1. Jianping Dong
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Correspondence to Gexiang Zhang.

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Dong, J., Zhang, G., Xiao, D. et al. Migration strategy in distributed adaptive optimization spiking neural P systems. J Membr Comput 4, 314–328 (2022). https://doi.org/10.1007/s41965-022-00117-2

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