Improved dung beetle optimizer algorithm for solving aircraft rerouting route planning under thunderstorm conditions

doi:10.16265/j.cnki.issn1003-3033.2025.12.0263

Abstract

Abstract:

To enhance the safety and on-time performance of flight rerouting under thunderstorm conditions, the airspace was first discretized into grids. Considering meteorological avoidance zones, aircraft performance, and fuel consumption, an ARRPTC model incorporating multiple safety factors was established based on the aircraft's origin, destination, and departure time. The model determined both the aircraft's airborne holding time at the departure point and its optimal rerouting path, with the objective of minimizing the total flight time. Subsequently, a multi-strategy improved DBO (MSDBO) was designed according to the problem characteristics to improve convergence speed and accuracy while avoiding premature convergence of the algorithm. The performance of the proposed MSDBO was then compared with four other swarm intelligence algorithms by means of six benchmark test functions to verify the effectiveness of the improvement strategies. Finally, a case study based on an actual aircraft rerouting scenario under thunderstorm conditions was conducted. The results indicate that, compared with traditional models, the proposed ARRPTC model reduces the detour time and distance by 52.3% and 53.0%, respectively, while the total flight time and distance decrease by 16.5% and 14.3%, and fuel consumption is reduced by 13.6%. Compared with the other four swarm intelligence algorithms, MSDBO demonstrates faster convergence, stronger global exploration, and superior local exploitation capabilities. In the ARRPTC model, MSDBO can obtain more optimal flight trajectories, significantly improving the efficiency of flight rerouting. Furthermore, as the thunderstorm safety threshold and turbulence coefficient threshold decrease, the total rerouting distance tends to increase.

Key words: aircraft rerouting route planning under thunderstorm conditions (ARRPTC), dung beetle optimizer (DBO), spiral search, swarm intelligence, mixed distribution perturbation

CLC Number:

X949

WEI Ming, GAO Anming, ZHANG Xiaoxiao. Improved dung beetle optimizer algorithm for solving aircraft rerouting route planning under thunderstorm conditions[J]. China Safety Science Journal, 2025, 35(12): 44-52.

Figures/Tables 12

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函数类别	序号	基准测试函数名称	最优值
单峰函数	F₁	Rotated Hyper-Ellipsoid Function	0
	F₂	Zakharov Function	0
	F₃	Sum Squares Function	0
多峰函数	F₄	Levy Function	0
	F₅	Six-Hump Camel Function	-1.031 6
	F₆	Schaffer Function N.4	0.292 58

测试函数	统计值	MSDBO	DBO	GWO	PSO	MPA
F₁	标准差	0.00	1.13×10^-128	7.89×10^-33	1 568.27	3.13×10^-5
F₁	平均值	0.00	2.06×10^-129	3.99×10^-33	2.87×10^-5	5.87×10^-30
F₂	标准差	0.00	1.17×10^-120	3.84×10^-36	6.09×10^-4	1.64×10^-8
F₂	平均值	0.00	2.13×10^-90	2.08×10^-7	4.63×10^-5	2.91×10^-6
F₃	标准差	0.00	9.04×10^-127	6.79×10^-35	80.52	4.20×10^-7
F₃	平均值	0.00	1.65×10^-127	5.33×10^-28	2.01×10^-4	1.13×10^-25
F₄	标准差	7.46×10^-9	3.60×10^-1	2.00×10^-1	7.78	2.02×10^-7
F₄	平均值	4.09×10^-9	3.34×10^-1	1.23	2.46	2.61×10^-1
F₅	标准差	4.52×10^-16	4.52×10^-16	1.68×10^-8	4.52×10^-16	4.52×10^-16
F₅	平均值	-1.03	-9.23×10^-1	-8.32×10^-1	-8.76×10^-1	-9.89×10^-1
F₆	标准差	6.35×10^-17	6.35×10^-17	1.47×10^-7	6.60×10^-17	2.20×10^-6
F₆	平均值	2.90×10^-1	3.50×10^-1	3.20×10^-1	3.10×10^-1	3.10×10^-1

测试函数	DBO	GWO	PSO	MPA
F₁	1.212×10^-12	1.212×10^-12	1.212×10^-12	1.212×10^-12
F₂	1.212×10^-12	1.212×10^-12	1.212×10^-12	1.212×10^-12
F₃	1.212×10^-12	1.212×10^-12	1.212×10^-12	1.212×10^-12
F₄	3.020×10^-11	3.020×10^-11	3.020×10^-11	3.020×10^-11
F₅	3.337×10^-2	3.337×10^-2	6.552×10^-12	3.337×10^-2
F₆	1.212×10^-12	1.212×10^-12	1.212×10^-12	1.212×10^-12
+/=/-	6/0/0	6/0/0	6/0/0	6/0/0

算法	平均迭代次数	平均目标值/min	MSDBO目标值占比/%	MSDBO收敛迭代次数占比/%	油耗成本/kg	里程/km
MSDBO	32	85	100	100	4 139	932.11
DBO	85	124	68.3	37.6	6 126	1 212.14
GWO	73	105	80.8	43.8	5 166	1 050.12
PSO	45	148	57.2	71.1	7 444	1 176.80
MPA	40	105	80.5	80.0	5 180	1 070.31

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