An enhanced Pied kingfisher optimizer for UAV path planning and engineering design problems DOI
D.Z. Fang, Quan Zhou

Published: May 13, 2025

Abstract The Pied Kingfisher Optimizer (PKO) is an advanced optimization algorithm. Its slow convergence and propensity to become stuck in local optima are its drawbacks, though. We suggest Enhanced algorithm (EPKO) overcome these drawbacks. In order enhance the algorithm's exploratory position modifications make it easier identify global optimum, tent mapping adaptive T-distribution control approach used. Additionally, we present a Cauchy mutation method, which gives individuals strong ability avoid extrema guide population more advantageous directions. improve optimizer's search performance greatly boost accuracy, speed, stability for solving complicated issues, leader-based boundary technique also suggested. compare EPKO's against eight well-known algorithms number of dimensions using 29 CEC2017 benchmark functions. efficacy EPKO was demonstrated by fact that our came out on top every comparison. mathematically modeled UAV used variety competitor address path planning problem assess suggested method's practicality. tackled three engineering design challenges several methods. results show has best performance. When comes solution quality stability, generally performs better than competitors, demonstrating greater application potential.

Language: Английский

Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification DOI Creative Commons
Yu Zhu, Mingxu Zhang, Qing Huang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 30, 2025

Abstract The classification of chronic diseases has long been a prominent research focus in the field public health, with widespread application machine learning algorithms. Diabetes is one high prevalence worldwide and considered disease its own right. Given nature this condition, numerous researchers are striving to develop robust algorithms for accurate classification. This study introduces revolutionary approach accurately classifying diabetes, aiming provide new methodologies. An improved Secretary Bird Optimization Algorithm (QHSBOA) proposed combination Kernel Extreme Learning Machine (KELM) diabetes prediction model. First, (SBOA) enhanced by integrating particle swarm optimization search mechanism, dynamic boundary adjustments based on optimal individuals, quantum computing-based t-distribution variations. performance QHSBOA validated using CEC2017 benchmark suite. Subsequently, used optimize kernel penalty parameter $$\:C$$ bandwidth $$\:c$$ KELM. Comparative experiments other models conducted datasets. experimental results indicate that QHSBOA-KELM model outperforms comparative four evaluation metrics: accuracy (ACC), Matthews correlation coefficient (MCC), sensitivity, specificity. offers an effective method early diagnosis diabetes.

Language: Английский

Citations

1
An improved genetic algorithm with wavelet packet and low-pass filters for reducing pressure and flow pulsations in axial piston motors DOI
Xüna Zhao, Fenglei Li,

Y. C. Zhu

et al.

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 12, 2025

Piston motors are crucial actuators in hydraulic systems, known for their high torque, precision, and stability. However, noise vibration caused by pressure flow pulsations limit the operating performance. To address this issue, a mechanical-hydraulic model was developed AMESim, area valve plate optimized.Experimental validation confirmed accuracy of effectiveness optimization. It found that position ([Formula: see text], [Formula: text]) shape parameters damping grooves distribution significantly affect pulsation. A new pulsation evaluation algorithm using wavelet packet decomposition low-pass filtering proposed. An improved adaptive genetic implemented MATLAB then used alongside AMESim to optimize these parameters. The optimal results demonstrated 17.02% reduction 74.09% simulation compared experimental motor.

Language: Английский

Citations

0
An enhanced Pied kingfisher optimizer for UAV path planning and engineering design problems DOI
D.Z. Fang, Quan Zhou

Published: May 13, 2025

Abstract The Pied Kingfisher Optimizer (PKO) is an advanced optimization algorithm. Its slow convergence and propensity to become stuck in local optima are its drawbacks, though. We suggest Enhanced algorithm (EPKO) overcome these drawbacks. In order enhance the algorithm's exploratory position modifications make it easier identify global optimum, tent mapping adaptive T-distribution control approach used. Additionally, we present a Cauchy mutation method, which gives individuals strong ability avoid extrema guide population more advantageous directions. improve optimizer's search performance greatly boost accuracy, speed, stability for solving complicated issues, leader-based boundary technique also suggested. compare EPKO's against eight well-known algorithms number of dimensions using 29 CEC2017 benchmark functions. efficacy EPKO was demonstrated by fact that our came out on top every comparison. mathematically modeled UAV used variety competitor address path planning problem assess suggested method's practicality. tackled three engineering design challenges several methods. results show has best performance. When comes solution quality stability, generally performs better than competitors, demonstrating greater application potential.

Language: Английский

Citations

0