Neural Network-Based Information Transfer for Dynamic Optimization

Xiao-Fang LIU, Zhi-Hui ZHAN, Tian-Long GU, Sam KWONG, Zhenyu LU, Henry Been-Lirn DUH, Jun ZHANG

Research output: Journal PublicationsJournal Article (refereed)peer-review

73 Citations (Scopus)


In dynamic optimization problems (DOPs), as the environment changes through time, the optima also dynamically change. How to adapt to the dynamic environment and quickly find the optima in all environments is a challenging issue in solving DOPs. Usually, a new environment is strongly relevant to its previous environment. If we know how it changes from the previous environment to the new one, then we can transfer the information of the previous environment, e.g., past solutions, to get new promising information of the new environment, e.g., new high-quality solutions. Thus, in this paper, we propose a neural network (NN)-based information transfer method, named NNIT, to learn the transfer model of environment changes by NN and then use the learned model to reuse the past solutions. When the environment changes, NNIT first collects the solutions from both the previous environment and the new environment and then uses an NN to learn the transfer model from these solutions. After that, the NN is used to transfer the past solutions to new promising solutions for assisting the optimization in the new environment. The proposed NNIT can be incorporated into population-based evolutionary algorithms (EAs) to solve DOPs. Several typical state-of-the-art EAs for DOPs are selected for comprehensive study and evaluated using the widely used moving peaks benchmark. The experimental results show that the proposed NNIT is promising and can accelerate algorithm convergence.
Original languageEnglish
Pages (from-to)1557-1570
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number5
Early online date19 Jul 2019
Publication statusPublished - May 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the Outstanding Youth Science Foundation under Grant 61822602, in part by the National Natural Science Foundations of China (NSFC) under Grant 61772207, Grant 61873097, and Grant 61773220, in part by the Natural Science Foundations of Guangdong Province for Distinguished Young Scholars under Grant 2014A030306038, in part by the Guangdong Natural Science Foundation Research Team under Grant 2018B030312003, in part by the Guangdong-Hong Kong Joint Innovation Platform under Grant 2018B050502006, and in part by the Hong Kong GRF-RGC General Research Fund 9042489 (CityU 11206317).


  • Dynamic optimization problem (DOP)
  • information transfer
  • neural network (NN)


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