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Optimization control method for industrial Internet of Things based on biological adaptive coevolutionary

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Abstract

Inspired by the collaborative mechanism among biological nervous, endocrine and immune systems, this paper proposes an algorithm of adaptive evolutionary based on biological cooperation (BCAE). This method can solve the dynamic multi-objective optimization problem of Industrial Internet of Things (IIoT) services to reduce the total service cost and service time. The BCAE algorithm consists of two parts: bottom level and top level. In the bottom level, different Pareto frontiers are obtained by coevolution of multiple subpopulations. In the top level, according to the distance between the service request and the service provider and the unit energy consumption of the service provider, the connection weight sequence is designed, and then the affinity matrix is constructed according to the connection weight sequence. Finally, the multi factor genetic algorithm (MFEA-II) is used to mate and imitate the service providers with different affinity, and the total service cost and total service time of the optimal solution are obtained, which are recorded in the top-level optimal antigen solution set. On the basis of single service strategy and collaborative service strategy, the IIoT services with dynamic requests are studied under different distributions. The obtained simulation results show that the performance of BCAE is better than the performance of the four existing algorithms, especially when solving high-dimensional problems.

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Acknowledgements

This work was supported in part by the National Key Research and Development Plan from Ministry of Science and Technology (2016YFB0302701), National Natural Science Foundation of China (61903078), Natural Science Foundation of Shanghai (19ZR1402300, 20ZR1400400), Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University CUSF-DH-D-2019090, and in part by the Special excellent Ph.D. International visit Program by the DHU.

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  1. Lei Chen

    Present address: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G1H9, Canada

Authors and Affiliations

  1. Department of Information Sciences and Technology, Donghua University, Shanghai, 201620, China

    Chunli Jiang, Kuangrong Hao, Witold Pedrycz & Xin Cai

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  2. Kuangrong Hao
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Correspondence to Kuangrong Hao or Witold Pedrycz.

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Jiang, C., Hao, K., Pedrycz, W. et al. Optimization control method for industrial Internet of Things based on biological adaptive coevolutionary. Wireless Netw 27, 5145–5160 (2021). https://doi.org/10.1007/s11276-021-02783-z

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