Power Transmission Network Optimization Strategy Based on Random Fractal Beetle Antenna Algorithm

IF 1.6 Q4 ENERGY & FUELS Wireless Power Transfer Pub Date : 2023-08-22 DOI:10.1155/2023/5255617
Junlei Liu, Zhu Chao, Xiangzhen He, Bo Bao, Xiaowen Lai
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Abstract

In order to optimize the performance of the transmission network (TN), this paper introduces the random fractal search algorithm based on the beetle antenna search algorithm, thus proposing the random fractal beetle antenna algorithm (SFBA). The main work of this research is as follows: (1) in the beetle antenna search algorithm, this study used a population of beetles and introduced elite members of the population in order to make the algorithm more stable and to some extent improve the accuracy of its answers. (2) Utilizing the elite reverse learning method and the leader’s multilearning strategy for elites helps to strike a balance between the global exploration and local development of the algorithm. This strategy also further improves the ability of the algorithm to find the optimal solution. (3) Experiments on real experimental data show that the SFBA algorithm proposed in this paper is effective in improving TN performance. In summary, the research content of this paper provides a good reference value for the performance optimization of TN in actual production.
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基于随机分形甲虫天线算法的输电网优化策略
为了优化传输网络(TN)的性能,本文在甲虫天线搜索算法的基础上引入了随机分形搜索算法,从而提出了随机分形甲虫天线算法(SFBA)。本研究的主要工作如下:(1)在甲虫天线搜索算法中,本研究使用了一个甲虫群体,并引入了该群体的精英成员,以使算法更加稳定,并在一定程度上提高其答案的准确性。(2) 利用精英反向学习方法和领导者对精英的多重学习策略,有助于在算法的全局探索和局部开发之间取得平衡。该策略还进一步提高了算法寻找最优解的能力。(3) 实际实验数据表明,本文提出的SFBA算法在提高TN性能方面是有效的。综上所述,本文的研究内容对TN在实际生产中的性能优化具有很好的参考价值。
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来源期刊
Wireless Power Transfer
Wireless Power Transfer ENERGY & FUELS-
CiteScore
2.50
自引率
0.00%
发文量
3
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