An Improved Hybrid Aquila Optimizer and Pigeon-Inspired Optimization Algorithm with Its Application in PID Parameter Optimization

To improve the optimization performance of the standard Aquila Optimizer (AO), an Improved Hybrid Aquila Optimizer and Pigeon-Inspired Optimization (IHAOPIO) algorithm based on stochastic balancing factor and Dimension-by-dimension Centroid Opposition-Based Learning (DCOBL) strategy is proposed. Wil...

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Vydáno v:	2024 8th International Symposium on Computer Science and Intelligent Control (ISCSIC) s. 367 - 371
Hlavní autoři:	Chen, Dongning, Du, Xinwei, Wang, Haowen, Xian, Qinggui, Sha, Jianhao, Yao, Chengyu
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 06.09.2024
Témata:	Accuracy Aquila Optimizer Benchmark testing Centroid Opposition-based Learning Computer science Intelligent control Optimization PID parameter optimization Pigeon-inspired Optimization Servomotors
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Popis
Shrnutí:	To improve the optimization performance of the standard Aquila Optimizer (AO), an Improved Hybrid Aquila Optimizer and Pigeon-Inspired Optimization (IHAOPIO) algorithm based on stochastic balancing factor and Dimension-by-dimension Centroid Opposition-Based Learning (DCOBL) strategy is proposed. Wilcoxon test is introduced to statistically analyze the data of six classical benchmark functions. Compared with several other optimization algorithms, the proposed IHAOPIO algorithm has better optimization capability in accuracy. The improved algorithm is used to the PID parameter optimization of one electro-hydraulic position servo system and achieves good control effect.
DOI:	10.1109/ISCSIC64297.2024.00082