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The traditional heuristic methods are slow, have low accuracy and are difficult to provide collision⁃free paths quickly in complex 3D environments, so an improved lemur optimization (ILO) algorithm is proposed. This algorithm integrates the advantages of the crayfish optimization algorithm (COA). The parameter C in the COA is adaptively decreased nonlinearly, and the Lévy flight step size is incorporated into the COA. The jump rate of the original LO algorithm is updated smoothly by the Sigmoid function. A Gaussian function model is utilized to simulate the mountain environment. The objective function establishes a mathematical model of unmanned aerial vehicle (UAV) flight based on the constraint conditions. The fitness function is used to determine the minimum cost of flight that can avoid obstacles within a specified airspace, and cubic spline interpolation is employed to smooth the flight path. Performance tests of the ILO algorithm were conducted on the test set CEC2017. The comparative results of multi⁃algorithm UAV path planning experiments show that the ILO algorithm generates high⁃quality and smooth paths in less iterations, overcomes the premature convergence and insufficient local search ability of the traditional genetic algorithms, adapts to complex terrain, and has reliable performance, so it provides an efficient solution for solving the UAV 3D path planning.
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Basic Information:
DOI：10.16652/j.issn.1004⁃373x.2026.09.028
Citation Information:
[1]Cheng Qing, Hu Haoxuan, Hu Wenhai.Improved lemur optimisation algorithm for solving UAV 3D path planning in mountainous areas[J].Modern Electronic Technique,2026(9):191-198.DOI:10.16652/j.issn.1004⁃373x.2026.09.028.
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