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Distributed hybrid optimization for multi-agent systems

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Abstract

This paper addresses the distributed optimization problems of multi-agent systems using a distributed hybrid impulsive protocol. The objective is to ensure the agents achieve the state consensus and optimize the aggregate objective functions assigned for each agent with distributed manner. We establish two criteria related to the optimality condition and the impulsive gain upper estimation, and propose a distributed hybrid impulsive optimal protocol, which includes two terms: the local averaging term in the continuous interval and the term involving the gradient information at impulsive instants. The simulation results show that the optimal consensus can be realized under the distributed hybrid impulsive optimization algorithm.

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Authors and Affiliations

  1. School of Mathematics, Southeast University, Nanjing, 210096, China

    XueGang Tan & JinDe Cao

  2. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China

    XueGang Tan, Yang Yuan & WangLi He

  3. Department of Science, Texas A&M University at Qatar, Doha, 23874, Qatar

    TingWen Huang

Authors
  1. XueGang Tan
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  2. Yang Yuan
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  3. WangLi He
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  4. JinDe Cao
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  5. TingWen Huang
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Corresponding authors

Correspondence to XueGang Tan or JinDe Cao.

Additional information

This work was supported in part by the National Key Research and Development Program of China (Grant No. 2020YFA0714300), the National Natural Science Foundation of China (Grant Nos. 61833005 and 62003084), the Fundamental Research Funds for the Central Universities, the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence (Grant No. BM2017002).

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Tan, X., Yuan, Y., He, W. et al. Distributed hybrid optimization for multi-agent systems. Sci. China Technol. Sci. 65, 1651–1660 (2022). https://doi.org/10.1007/s11431-022-2060-7

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  • DOI: https://doi.org/10.1007/s11431-022-2060-7

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