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Neural Network-Aided Differential Evolution With Double Q-Learning for Dynamic Optimization
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AI
DOI：10.1109/TCSS.2026.3664664.png)
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Abstract
En 中文
As the search space and hence the optimum vary through time, dynamic optimization problems (DOPs) bring tremendous difficulties. Changes in DOPs often manifest as diverse dynamics. Consequently, regulating individuals’ search to adapt to diverse dynamics is crucial to tackle DOPs. Besides, due to the inherent population nature in dynamic optimization algorithms (DOAs), loss of global and local diversities is a critical issue deteriorating the performance of DOAs. Faced with these difficulties, this article proposes a neural network-aided differential evolution with double Q-learning (NNDE-DQ), in which an evolutionary regulation network (ERN) is designed to maintain high global and local diversities over time and regulate individuals’ search that can adapt to diverse dynamics. NNDE-DQ first partitions the search space into multiple subspaces and in each subspace distant individuals are selected as the centers of ERN’s hidden nodes activated by radial basis function. Every input individual is mutated with two randomly selected hidden node centers from different subspaces as differential individuals, facilitating the maintenance of a high global diversity due to very distinct differential terms of the population. Moreover, each mutated individual selects a hidden node center from the subspace located by the mutated individual to undergo crossover. Due to distant hidden node centers in each subspace, a high local diversity can be maintained by individuals’ crossover. Besides, DQ is introduced to acquire ERN’s desired output by interactively estimating individuals’ state-action information, enabling ERN to learn the regulation of individuals’ search in dynamic environments and hence adapt to diverse dynamics. The experimental results demonstrate that NNDE-DQ significantly improves the performance in solving various DOPs comparing to seven state-of-the-art DOAs.
Keywords:
Differential evolution (DE)
dynamic optimization
neural network
Q-learning
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