An exploration-enhanced hybrid algorithm based on regularity evolution for multi-objective multi-UAV 3-D path planning

Path planning poses a complex optimization challenge essential for the safe operation and successful mission execution of unmanned aerial vehicles (UAVs). Developing objectives, constraints, and decision-making processes for three-dimensional path planning involving multiple UAVs presents substantia...

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Vydáno v:Complex & intelligent systems Ročník 11; číslo 5; s. 225 - 20
Hlavní autoři: Bai, Zhenzu, Zhou, Haiyin, Wei, Juhui, Zhou, Xuanying, Ning, Yida, Wang, Jiongqi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.05.2025
Springer Nature B.V
Springer
Témata:
Algorithms
Complexity
Computational Intelligence
Constrained multi-objective evolutionary algorithm
Constrained multi-objective problems
Data Structures and Information Theory
Engineering
Evolutionary algorithms
Feasibility studies
Multiple objective analysis
Optimization
Original Article
Path planning
Regularity
Regularity evolution
Unmanned aerial vehicles
Constrained multi-objective problems
Path planning
Constrained multi-objective evolutionary algorithm
Regularity evolution
ISSN:2199-4536, 2198-6053
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Shrnutí:Path planning poses a complex optimization challenge essential for the safe operation and successful mission execution of unmanned aerial vehicles (UAVs). Developing objectives, constraints, and decision-making processes for three-dimensional path planning involving multiple UAVs presents substantial challenges within the multi-objective optimization community. Traditional modeling approaches primarily aim to minimize path length and collision risks, often overlooking the need for a quantitative assessment of communication quality among UAVs. This neglect causes an inadequate representation of their true cooperative capabilities. In addition, there is difficulty in achieving an optimal balance between convergence, diversity, and feasibility. Therefore, this study introduces a bi-objective, three-dimensional path planning model specifically designed for UAVs. This model features an objective function that quantitatively evaluates inter-UAV communication quality throughout their flights. To solve this problem, this study proposes the dual-population regularity evolution algorithm (DPREA), which incorporates an auto-switching regularity evolutionary reproduction operator known as autoRE. It conducts extensive experiments across six testing suites and three path-planning simulation cases to validate the effectiveness of DPREA. The simulation results showed that its performance in addressing constrained multi-objective problems is significantly superior or at least comparable to that of state-of-the-art algorithms in most instances.
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ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-025-01846-4