UAV Path Planning for Forest Firefighting Using Optimized Multi-Objective Jellyfish Search Algorithm

Rui Zeng, Runteng Luo and Bin Liu ()
Additional contact information
Rui Zeng: School of Computer Science and School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China
Runteng Luo: School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
Bin Liu: School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China

Mathematics, 2025, vol. 13, issue 17, 1-26

Abstract: This paper presents a novel approach to address the challenges of complex terrain, dynamic wind fields, and multi-objective constraints in multi-UAV collaborative path planning for forest firefighting missions. An extensible algorithm, termed Parallel Vectorized Differential Evolution-based Multi-Objective Jellyfish Search (PVDE-MOJS), is proposed to enhance path planning performance. A comprehensive multi-objective cost function is formulated, incorporating path length, threat avoidance, altitude constraints, path smoothness, and wind effects. Forest-specific constraints are modeled using cylindrical threat zones and segmented wind fields. The conventional jellyfish search algorithm is then enhanced through multi-core parallel fitness evaluation, vectorized non-dominated sorting, and differential evolution-based mutation. These improvements substantially boost convergence efficiency and solution quality in high-dimensional optimization scenarios. Simulation results on the Phillip Archipelago Forest Farm digital elevation model (DEM) in Australia demonstrate that PVDE-MOJS outperforms the original MOJS algorithm in terms of inverted generational distance (IGD) across benchmark functions UF1–UF10. The proposed method achieves effective obstacle avoidance, altitude optimization, and wind adaptation, producing uniformly distributed Pareto fronts. This work offers a viable solution for emergency UAV path planning in forest fire rescue scenarios, with future extensions aimed at dynamic environments and large-scale UAV swarms.

Keywords: multi-UAV collaboration; path planning; jellyfish search algorithm; multi-objective optimization; forest firefighting; wind field modeling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/17/2745/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/17/2745/ (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:13:y:2025:i:17:p:2745-:d:1732775

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 ().