Adaptive differential evolution for efficient unmanned aerial vehicle path planning in different environments

Chen Dewang; Chen Xiubin; Meng Zhenyu; Bahattin Türetken

doi:10.11959/j.issn.2096-6652.ZNKX26047

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Adaptive differential evolution for efficient unmanned aerial vehicle path planning in different environments

Papers and Reports | 更新时间：2026-07-29

- Adaptive differential evolution for efficient unmanned aerial vehicle path planning in different environments
- Chinese Journal of Intelligent Science and Technology Vol. 8, Issue 2, Pages: 250-263(2026)
- 作者机构：
  
  1.福建理工大学计算机与数据科学学院，福建福州 350000
  2.福建理工大学人工智能与交通工程学院，福建福州 350000
  3.科贾埃利大学工程学院，土耳其科贾埃利 41030
- 作者简介：
- 基金信息：
  
  The Innovation Star Talent Program of the Third Batch in Fujian Province(GY-Z23068)
- DOI：10.11959/j.issn.2096-6652.ZNKX26047
  CLC： TP39
- Received：13 April 2026，
  
  Revised：2026-07-03，
  
  Accepted：09 July 2026，
  
  Online First：27 July 2026，
  
  Published：15 June 2026
- 稿件说明：
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陈德旺,陈秀彬,孟振宇等.自适应差分进化算法在不同环境下实现高效无人机路径规划[J].智能科学与技术学报,2026,08(02):250-263.

Chen Dewang,Chen Xiubin,Meng Zhenyu,et al.Adaptive differential evolution for efficient unmanned aerial vehicle path planning in different environments[J].Chinese Journal of Intelligent Science and Technology,2026,08(02):250-263.
陈德旺,陈秀彬,孟振宇等.自适应差分进化算法在不同环境下实现高效无人机路径规划[J].智能科学与技术学报,2026,08(02):250-263. DOI： 10.11959/j.issn.2096-6652.ZNKX26047.

Chen Dewang,Chen Xiubin,Meng Zhenyu,et al.Adaptive differential evolution for efficient unmanned aerial vehicle path planning in different environments[J].Chinese Journal of Intelligent Science and Technology,2026,08(02):250-263. DOI： 10.11959/j.issn.2096-6652.ZNKX26047.

摘要

无人机（UAV）路径规划问题因搜索空间的高维特性及复杂操作约束条件而极具挑战性。为突破此局限，提出一种融合三项协同策略的改进型差分进化（DE），首先引入基于泊松分布的机制判断下一代突变成功率，并自适应地将搜索资源优先分配给更有效的突变策略；其次采用余弦相似性约束引导种群演化方向，减少方向性分散并提升收敛效率；最后设计分阶段自适应参数控制策略，在初期强化全局探索能力，后期加速收敛进程，同时保持种群多样性。通过协同整合自适应策略选择、演化方向引导与动态参数调整，所提算法实现了探索与开发之间的平衡。在CEC2017基准测试集及基于真实数字高程模型（DEM）数据构建的多个无人机路径规划仿真场景的实验中，所提算法在收敛速度、稳定性及解质量等方面展现出较好的竞争力。

Abstract

The unmanned aerial vehicle (UAV) path planning problem is highly challenging due to the high dimensionality of the search space and presence of complex operational constraints. To address these limitations

an enhanced differential evolution (DE) algorithm was proposed that integrates three synergistic strategies. First

a Poisson distribution based mechanism was introduced to estimate the mutation success rate in the next generation

enabling adaptive allocation of search resources toward more effective mutation strategies. Second

cosine similarity constraint was employed to guide the evolutionary direction of the population

thereby reducing directional divergence and improving convergence efficiency. Third

a staged adaptive parameter control scheme was designed

which enhances global exploration in the early phase and accelerates convergence in later phase while maintaining population diversity. By collaboratively integrating adaptive strategy selection

evolutionary direction guidance

and dynamic parameter adjustment

the proposed method achieves a balanced trade off between exploration and exploitation. Experimental results of the CEC2017 benchmark suite and multiple UAV path planning simulation scenarios constructed from real digital elevation model (DEM) data demonstrate that the proposed algorithm achieves competitive performance in convergence speed

stability

and solution quality.

关键词

Keywords

references

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