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A Hybrid Artificial Bee Colony and Differential Evolution for UAV Path Planning in Complex Environments.

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Title: A Hybrid Artificial Bee Colony and Differential Evolution for UAV Path Planning in Complex Environments.
Authors: Wang, Yunming1 (AUTHOR) wang19871128@126.com, Sun, Zhanpeng1 (AUTHOR) 1429478116@qq.com, Chen, Shifeng2 (AUTHOR) chens@lingnan.edu.cn, Chen, Bo2 (AUTHOR) chenbo@lingnan.edu.cn
Source: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). Aug2026, p1-14. 14p.
Subject Terms: *Bees algorithm, *Differential evolution, *Simulation methods & models, *Robotic path planning, *Mathematical optimization, *Metaheuristic algorithms, *Landscapes
Abstract: Unmanned aerial vehicle (UAV) global path planning in static environments aims to find the optimal or feasible path between the starting and ending points, which is related to the efficiency and safety of UAV’s task execution. Thus, UAV path planning has received wide attention. Existing path planning methods such as the artificial bee colony (ABC) algorithm are susceptible to local optima and suffer from insufficient local exploitation. To address these problems, this paper proposes a novel UAV path planning method that combines the ABC algorithm with differential evolution (DE), referred to as ABCDE. First, during the initialization phase, the DE/rand/1 strategy is incorporated to optimize the initial population, thereby reducing invalid solutions. Second, in the scout bee phase, a hybrid approach that combines the DE/rand/1 and DE/best/1 strategies is adopted, with strategy selection dynamically adapted to the algorithm’s iteration progress. This effectively addresses the problems of weak local exploitation capability and susceptibility to local optima. Finally, simulation experiments are conducted, including randomly generated urban and mountainous terrain scenarios, to perform a comparative analysis of path planning performance across different scenes and algorithms. Experimental results show that compared with the ABC, the PSO, the GA, and the improved ABC (IABC) algorithms, the proposed method reduces path lengths by 4.33%, 3.15%, 17.04%, and 2.40%, respectively, in both urban and mountainous terrain environments and improves the quality of path planning and the local exploitation capability. [ABSTRACT FROM AUTHOR]
Database: Academic Search Index
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  – Url: https://dx.doi.org/doi:10.1007/s13369-026-11574-7
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DbLabel: Academic Search Index
An: 196264144
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PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1452.42504882813
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Hybrid Artificial Bee Colony and Differential Evolution for UAV Path Planning in Complex Environments.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Yunming%22">Wang, Yunming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wang19871128@126.com</i><br /><searchLink fieldCode="AR" term="%22Sun%2C+Zhanpeng%22">Sun, Zhanpeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 1429478116@qq.com</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Shifeng%22">Chen, Shifeng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> chens@lingnan.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Bo%22">Chen, Bo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> chenbo@lingnan.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Arabian+Journal+for+Science+%26+Engineering+%28Springer+Science+%26+Business+Media+B%2EV%2E+%29%22">Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. )</searchLink>. Aug2026, p1-14. 14p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Bees+algorithm%22">Bees algorithm</searchLink><br />*<searchLink fieldCode="DE" term="%22Differential+evolution%22">Differential evolution</searchLink><br />*<searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br />*<searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Landscapes%22">Landscapes</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Unmanned aerial vehicle (UAV) global path planning in static environments aims to find the optimal or feasible path between the starting and ending points, which is related to the efficiency and safety of UAV’s task execution. Thus, UAV path planning has received wide attention. Existing path planning methods such as the artificial bee colony (ABC) algorithm are susceptible to local optima and suffer from insufficient local exploitation. To address these problems, this paper proposes a novel UAV path planning method that combines the ABC algorithm with differential evolution (DE), referred to as ABCDE. First, during the initialization phase, the DE/rand/1 strategy is incorporated to optimize the initial population, thereby reducing invalid solutions. Second, in the scout bee phase, a hybrid approach that combines the DE/rand/1 and DE/best/1 strategies is adopted, with strategy selection dynamically adapted to the algorithm’s iteration progress. This effectively addresses the problems of weak local exploitation capability and susceptibility to local optima. Finally, simulation experiments are conducted, including randomly generated urban and mountainous terrain scenarios, to perform a comparative analysis of path planning performance across different scenes and algorithms. Experimental results show that compared with the ABC, the PSO, the GA, and the improved ABC (IABC) algorithms, the proposed method reduces path lengths by 4.33%, 3.15%, 17.04%, and 2.40%, respectively, in both urban and mountainous terrain environments and improves the quality of path planning and the local exploitation capability. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asx&AN=196264144
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1007/s13369-026-11574-7
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 1
    Subjects:
      – SubjectFull: Bees algorithm
        Type: general
      – SubjectFull: Differential evolution
        Type: general
      – SubjectFull: Simulation methods & models
        Type: general
      – SubjectFull: Robotic path planning
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Landscapes
        Type: general
    Titles:
      – TitleFull: A Hybrid Artificial Bee Colony and Differential Evolution for UAV Path Planning in Complex Environments.
        Type: main
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          Name:
            NameFull: Wang, Yunming
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            NameFull: Sun, Zhanpeng
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          Name:
            NameFull: Chen, Shifeng
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            NameFull: Chen, Bo
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          Dates:
            – D: 18
              M: 08
              Text: Aug2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 2193567X
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            – TitleFull: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. )
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