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A decomposition-based many-objective artificial bee colony algorithm with reinforcement learning
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DOI：10.1016/j.asoc.2019.105879.png)
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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. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Swarm intelligence
Artificial bee colony
Many-objective optimization
Reinforcement learning
Decomposition strategy
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