期刊
BIOMIMETICS
卷 8, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/biomimetics8050400
关键词
binarization scheme selection; grey wolf optimizer; sine cosine algorithm; whale optimization algorithm; set covering problem; Q-learning; diversity metrics
This paper proposes an approach to solve binary combinatorial problems using continuous metaheuristics, focusing on the importance of binarization in the optimization process. Experimental results show that binarization rules have a greater impact on algorithm performance than transfer functions. It was found that sets of actions incorporating the elite or elite roulette binarization rule are the best.
In this work, an approach is proposed to solve binary combinatorial problems using continuous metaheuristics. It focuses on the importance of binarization in the optimization process, as it can have a significant impact on the performance of the algorithm. Different binarization schemes are presented and a set of actions, which combine different transfer functions and binarization rules, under a selector based on reinforcement learning is proposed. The experimental results show that the binarization rules have a greater impact than transfer functions on the performance of the algorithms and that some sets of actions are statistically better than others. In particular, it was found that sets that incorporate the elite or elite roulette binarization rule are the best. Furthermore, exploration and exploitation were analyzed through percentage graphs and a statistical test was performed to determine the best set of actions. Overall, this work provides a practical approach for the selection of binarization schemes in binary combinatorial problems and offers guidance for future research in this field.
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