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...

Full description

Saved in:
Bibliographic Details
Published in:2024 8th International Symposium on Computer Science and Intelligent Control (ISCSIC) pp. 367 - 371
Main Authors: Chen, Dongning, Du, Xinwei, Wang, Haowen, Xian, Qinggui, Sha, Jianhao, Yao, Chengyu
Format: Conference Proceeding
Language:English
Published: IEEE 06.09.2024
Subjects:
Accuracy
Aquila Optimizer
Benchmark testing
Centroid Opposition-based Learning
Computer science
Intelligent control
Optimization
PID parameter optimization
Pigeon-inspired Optimization
Servomotors
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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