ADE-ALA: An Adaptive Differential Evolution Artificial Lemming Algorithm for 3D UAV Path Planning

Zhenhan Hu; Junbao Li; Maqiang Zhai; Huanbei Ren; Han Liu; Wenke Huang

doi:10.1109/CCDC69976.2026.11560216

ADE-ALA: An Adaptive Differential Evolution Artificial Lemming Algorithm for 3D UAV Path Planning

Zhenhan Hu
, Junbao Li
, Maqiang Zhai
, Huanbei Ren
, Han Liu
, Wenke Huang

Faculty of Computing

Harbin Institute of Technology
Ltd.
North University of China
Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The previous literature on complex 3D UAV path planning can hardly prevent premature convergence and limited diversity, for which this paper investigates and improves an adaptive differential evolution artificial lemming algorithm (ADEALA). Based on the differential evolution artificial lemming algorithm (DE-ALA), the proposed ADE-ALA incorporates a differential evolution strategy to enhance local exploitation and solution precision, alongside a feedback-based stagnation handling mechanism to dynamically adjust the population to escape local optima, which is effective for preventing premature convergence and limited diversity. The simulation results demonstrate ADE-ALA's effectiveness, accuracy and robustness, compared with PSO, AOO, ALA, and DE-ALA.

Original language	English
Title of host publication	38th Chinese Control and Decision Conference, CCDC 2026
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3950-3955
Number of pages	6
ISBN (Electronic)	9798331550707
DOIs	https://doi.org/10.1109/CCDC69976.2026.11560216
State	Published - 2026
Event	38th Chinese Control and Decision Conference, CCDC 2026 - Nanjing, China Duration: 15 May 2026 → 18 May 2026

Publication series

Name	38th Chinese Control and Decision Conference, CCDC 2026

Conference

Conference	38th Chinese Control and Decision Conference, CCDC 2026
Country/Territory	China
City	Nanjing
Period	15/05/26 → 18/05/26

Keywords

UAV path planning
artificial lemming algorithm
differential evolution
metaheuristic optimization
stagnation handling mechanism

Access to Document

10.1109/CCDC69976.2026.11560216

Cite this

Hu, Z., Li, J., Zhai, M., Ren, H., Liu, H., & Huang, W. (2026). ADE-ALA: An Adaptive Differential Evolution Artificial Lemming Algorithm for 3D UAV Path Planning. In 38th Chinese Control and Decision Conference, CCDC 2026 (pp. 3950-3955). (38th Chinese Control and Decision Conference, CCDC 2026). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC69976.2026.11560216

@inproceedings{11deefc1e5074d30918fb4da4cdec27c,

title = "ADE-ALA: An Adaptive Differential Evolution Artificial Lemming Algorithm for 3D UAV Path Planning",

abstract = "The previous literature on complex 3D UAV path planning can hardly prevent premature convergence and limited diversity, for which this paper investigates and improves an adaptive differential evolution artificial lemming algorithm (ADEALA). Based on the differential evolution artificial lemming algorithm (DE-ALA), the proposed ADE-ALA incorporates a differential evolution strategy to enhance local exploitation and solution precision, alongside a feedback-based stagnation handling mechanism to dynamically adjust the population to escape local optima, which is effective for preventing premature convergence and limited diversity. The simulation results demonstrate ADE-ALA's effectiveness, accuracy and robustness, compared with PSO, AOO, ALA, and DE-ALA.",

keywords = "UAV path planning, artificial lemming algorithm, differential evolution, metaheuristic optimization, stagnation handling mechanism",

author = "Zhenhan Hu and Junbao Li and Maqiang Zhai and Huanbei Ren and Han Liu and Wenke Huang",

note = "Publisher Copyright: {\textcopyright} 2026 IEEE.; 38th Chinese Control and Decision Conference, CCDC 2026 ; Conference date: 15-05-2026 Through 18-05-2026",

year = "2026",

doi = "10.1109/CCDC69976.2026.11560216",

language = "英语",

series = "38th Chinese Control and Decision Conference, CCDC 2026",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3950--3955",

booktitle = "38th Chinese Control and Decision Conference, CCDC 2026",

address = "美国",

}

Hu, Z, Li, J, Zhai, M, Ren, H, Liu, H & Huang, W 2026, ADE-ALA: An Adaptive Differential Evolution Artificial Lemming Algorithm for 3D UAV Path Planning. in 38th Chinese Control and Decision Conference, CCDC 2026. 38th Chinese Control and Decision Conference, CCDC 2026, Institute of Electrical and Electronics Engineers Inc., pp. 3950-3955, 38th Chinese Control and Decision Conference, CCDC 2026, Nanjing, China, 15/05/26. https://doi.org/10.1109/CCDC69976.2026.11560216

ADE-ALA: An Adaptive Differential Evolution Artificial Lemming Algorithm for 3D UAV Path Planning. / Hu, Zhenhan; Li, Junbao; Zhai, Maqiang et al.
38th Chinese Control and Decision Conference, CCDC 2026. Institute of Electrical and Electronics Engineers Inc., 2026. p. 3950-3955 (38th Chinese Control and Decision Conference, CCDC 2026).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - ADE-ALA

T2 - 38th Chinese Control and Decision Conference, CCDC 2026

AU - Hu, Zhenhan

AU - Li, Junbao

AU - Zhai, Maqiang

AU - Ren, Huanbei

AU - Liu, Han

AU - Huang, Wenke

PY - 2026

Y1 - 2026

N2 - The previous literature on complex 3D UAV path planning can hardly prevent premature convergence and limited diversity, for which this paper investigates and improves an adaptive differential evolution artificial lemming algorithm (ADEALA). Based on the differential evolution artificial lemming algorithm (DE-ALA), the proposed ADE-ALA incorporates a differential evolution strategy to enhance local exploitation and solution precision, alongside a feedback-based stagnation handling mechanism to dynamically adjust the population to escape local optima, which is effective for preventing premature convergence and limited diversity. The simulation results demonstrate ADE-ALA's effectiveness, accuracy and robustness, compared with PSO, AOO, ALA, and DE-ALA.

AB - The previous literature on complex 3D UAV path planning can hardly prevent premature convergence and limited diversity, for which this paper investigates and improves an adaptive differential evolution artificial lemming algorithm (ADEALA). Based on the differential evolution artificial lemming algorithm (DE-ALA), the proposed ADE-ALA incorporates a differential evolution strategy to enhance local exploitation and solution precision, alongside a feedback-based stagnation handling mechanism to dynamically adjust the population to escape local optima, which is effective for preventing premature convergence and limited diversity. The simulation results demonstrate ADE-ALA's effectiveness, accuracy and robustness, compared with PSO, AOO, ALA, and DE-ALA.

KW - UAV path planning

KW - artificial lemming algorithm

KW - differential evolution

KW - metaheuristic optimization

KW - stagnation handling mechanism

UR - https://www.scopus.com/pages/publications/105043960790

U2 - 10.1109/CCDC69976.2026.11560216

DO - 10.1109/CCDC69976.2026.11560216

M3 - 会议稿件

AN - SCOPUS:105043960790

T3 - 38th Chinese Control and Decision Conference, CCDC 2026

SP - 3950

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BT - 38th Chinese Control and Decision Conference, CCDC 2026

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 15 May 2026 through 18 May 2026

ER -

Hu Z, Li J, Zhai M, Ren H, Liu H, Huang W. ADE-ALA: An Adaptive Differential Evolution Artificial Lemming Algorithm for 3D UAV Path Planning. In 38th Chinese Control and Decision Conference, CCDC 2026. Institute of Electrical and Electronics Engineers Inc. 2026. p. 3950-3955. (38th Chinese Control and Decision Conference, CCDC 2026). doi: 10.1109/CCDC69976.2026.11560216

ADE-ALA: An Adaptive Differential Evolution Artificial Lemming Algorithm for 3D UAV Path Planning

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