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An adaptive finite-time neurodynamic approach to distributed consensus-based optimization problem

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Abstract

In this paper, a novel distributed adaptive neurodynamic approach (DANA) based on proportional integral technique is proposed to solve distributed optimization problem on multi-agent systems. The goal is that all agents reach consensus in finite time and converge to the optimal solution of the global objective function in fixed time. In the proposed approach, the proportional technique drives all agents to reach consensus, and the integral technique is used to offset the influence of the gradient term of the objective function. On the other hand, in order to avoid the prior estimation of gain parameter and the global gradient information, as the main contribution of this paper, the adaptive idea is considered into proportional integral technique. The results show that the adaptive integral technique can automatically adjust the gain according to the maximum consensus error between agents, so as to ensure that agents can achieve consensus in finite time. Then the theoretical results are applied to voltage distribution and logistic regression. Numerical simulation verifies the effectiveness of DANA.

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The authors declare that data supporting the findings of this study are available within the article, the figures are concrete expression.

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Acknowledgements

This research is supported by the Doctoral Fund of Heilongjiang Institute of Technology (Grant No. 2017BJ26), the National Natural Science Foundation of China (Grant No. 62176073, No. 61773136), and the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology.

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Authors and Affiliations

  1. Department of Mathematics, Heilongjiang Institute of Technology, Harbin, People’s Republic of China

    Qingfa Li

  2. Department of Mathematics, Harbin Institute of Technology, Weihai, 264209, People’s Republic of China

    Mengxin Wang, Haowen Sun & Sitian Qin

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  1. Qingfa Li
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  2. Mengxin Wang
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  4. Sitian Qin
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Correspondence to Qingfa Li.

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Li, Q., Wang, M., Sun, H. et al. An adaptive finite-time neurodynamic approach to distributed consensus-based optimization problem. Neural Comput & Applic 35, 20841–20853 (2023). https://doi.org/10.1007/s00521-023-08794-5

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