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دسته بندی:
یادگیری تقویتی - Reinforcement-Learning
سال انتشار:
2020
عنوان انگلیسی مقاله:
A decomposition-based many-objective artificial bee colony algorithm with reinforcement learning
ترجمه فارسی عنوان مقاله:
یک الگوریتم مستعمره مصنوعی زنبورعسل مبتنی بر تجزیه با یادگیری تقویتی
منبع:
Sciencedirect - Elsevier - Applied Soft Computing Journal, 86 (2020) 105879: doi:10:1016/j:asoc:2019:105879
نویسنده:
Haitong Zhao a,1, Changsheng Zhang b,1,∗
چکیده انگلیسی:
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.
Keywords: Swarm intelligence | Artificial bee colony | Many-objective optimization | Reinforcement learning | Decomposition strategy
قیمت: رایگان
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