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• W2987122341 startingPage "105879" @default.
• W2987122341 abstract "When optimizing many-objective optimization problems (MaOPs), the optimization effect is normally related to the problem types. Therefore, enhancing the generalization ability is essential to the application of the algorithms. In this paper, a novel decomposition-based Artificial bee colony algorithm (ABC) for MaOP optimization, MaOABC/D-LA, is presented to enhance the generalization ability. A reinforcement learning-based searching strategy is designed in the MaOABC/D-LA, with which the algorithm adjusts its searching actions according to their performance. And a variant of the onlooker bee mechanism is proposed to balance the optimization quality. To investigate performance of the proposed algorithm, a comparison experiment is conducted. The experimental results show that the MaOABC/D-LA outperforms the peer algorithms in efficiency and solution quality for MaOPs with different types of features. This indicates the proposed method has a definite effect on improving generalization ability." @default.
• W2987122341 created "2019-11-22" @default.
• W2987122341 creator A5042815955 @default.
• W2987122341 creator A5068081202 @default.
• W2987122341 date "2020-01-01" @default.
• W2987122341 modified "2023-10-14" @default.
• W2987122341 title "A decomposition-based many-objective artificial bee colony algorithm with reinforcement learning" @default.
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• W2987122341 doi "https://doi.org/10.1016/j.asoc.2019.105879" @default.
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