Decentralized Differential Evolutionary Algorithm for Large-Scale Networked Systems

  • Guanghong Han
  • Xi ChenEmail author
  • Qianchuan Zhao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 890)


The optimization of a complex system with multiple subsystems is a tough problem. In this paper, a Decentralized differential evolutionary algorithm (DDEA) is proposed. The simulations for both DDEA and centralized DE on three benchmark functions are carried out. The numerical results show that DDEA is efficient to solve decentralized optimization problems. On these problems, the proposed DDEA outperforms centralized DE in convergence.


Optimization Decentralized System Subsystem Differential evolution 



This work was supported by National Key Research and Development Project of China (No. 2017YFC0704100 entitled New generation intelligent building platform techniques, and 2016YFB0901900), the National Natural Science Foundation of China (No. 61425027), the 111 International Collaboration Program of China under Grant B06002, and Special fund of Suzhou-Tsinghua Innovation Leading Action (Project Number: 2016SZ0202).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of AutomationCenter for Intelligent and Networked Systems (CFINS), Tsinghua UniversityBeijingChina

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