IEEE Trans Neural Netw Learn Syst. 2020 May;31(5):1557-1570. doi: 10.1109/TNNLS.2019.2920887. Epub 2019 Jul 19.
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.
在动态优化问题（DOP）中，随着时间的推移环境发生变化，最优解也随之动态变化。如何适应动态环境并在所有环境中快速找到最优解是解决 DOP 的一个具有挑战性的问题。通常，新环境与前一个环境密切相关。如果我们知道它是如何从前一个环境变化到新环境的，那么我们可以从前一个环境转移信息，例如过去的解，以获得新环境的新有前途的信息，例如新的高质量解。因此，在本文中，我们提出了一种基于神经网络（NN）的信息传递方法，称为 NNIT，通过 NN 学习环境变化的转移模型，然后使用学习到的模型重用过去的解。当环境发生变化时，NNIT 首先从前一个环境和新环境中收集解，然后使用 NN 从这些解中学习转移模型。之后，NN 用于将过去的解转移到新环境中具有潜力的新解，以协助新环境中的优化。所提出的 NNIT 可以被合并到基于种群的进化算法（EA）中，以解决 DOP。选择了几个最先进的用于 DOP 的 EA 进行综合研究，并使用广泛使用的移动峰基准进行评估。实验结果表明，所提出的 NNIT 很有前途，可以加速算法收敛。
