Mixed Multi-Strategy Improved Aquila Optimizer and Its Application in Path Planning

Tianyue Bao (), Jiaxin Zhao, Yanchang Liu, Xusheng Guo and Tianshuo Chen
Additional contact information
Tianyue Bao: Electrical Information Engineering Department, Northeast Petroleum University Qinhuangdao Campus, Qinhuangdao 066000, China
Jiaxin Zhao: Electrical Information Engineering Department, Northeast Petroleum University, Daqing 163000, China
Yanchang Liu: Electrical Information Engineering Department, Northeast Petroleum University Qinhuangdao Campus, Qinhuangdao 066000, China
Xusheng Guo: Electrical Information Engineering Department, Northeast Petroleum University, Daqing 163000, China
Tianshuo Chen: Electrical Information Engineering Department, Northeast Petroleum University Qinhuangdao Campus, Qinhuangdao 066000, China

Mathematics, 2024, vol. 12, issue 23, 1-19

Abstract: With the growing prevalence of drone technology across various sectors, efficient and safe path planning has emerged as a critical research priority. Traditional Aquila Optimizers, while effective, face limitations such as uneven population initialization, a tendency to get trapped in local optima, and slow convergence rates. This study presents a multi-strategy fusion of the improved Aquila Optimizer, aiming to enhance its performance by integrating diverse optimization techniques, particularly in the context of path planning. Key enhancements include the integration of Bernoulli chaotic mapping to improve initial population diversity, a spiral stepping strategy to boost search precision and diversity, and a “stealing” mechanism from the Dung Beetle Optimization algorithm to enhance global search capabilities and convergence. Additionally, a nonlinear balance factor is employed to dynamically manage the exploration–exploitation trade-off, thereby increasing the optimization of speed and accuracy. The effectiveness of the mixed multi-strategy improved Aquila Optimizer is validated through simulations on benchmark test functions, CEC2017 complex functions, and path planning scenarios. Comparative analysis with seven other optimization algorithms reveals that the proposed method significantly improves both convergence speed and optimization accuracy. These findings highlight the potential of mixed multi-strategy improved Aquila Optimizer in advancing drone path planning performance, offering enhanced safety and efficiency.

Keywords: multi-strategy integration; Aquila Optimizer; unmanned aerial vehicles path planning; optimization algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/23/3818/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/23/3818/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:23:p:3818-:d:1535201

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().