ArticleBiomimetics (Basel, Switzerland)2025
An Enhanced Artificial Lemming Algorithm and Its Application in UAV Path Planning.
Article in Biomimetics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Hybrid Multi-Strategy Chinese Pangolin Optimization Algorithm and Its Applications.Biomimetics (Basel, Switzerland) · 2026Article
- HALA: A Hybrid Dual-Population Optimizer Integrating an Enhanced Artificial Lemming Algorithm and SHADE.Biomimetics (Basel, Switzerland) · 2026Article
- Research on UAV Path Planning Based on Enhanced Artificial Lemming Algorithm.Biomimetics (Basel, Switzerland) · 2026Article
- Intelligent Control of Magnetic Ball Suspension Systems via a Novel Hyperbolic Tangent PID Controller Tuned by the Artificial Lemming Algorithm.Biomimetics (Basel, Switzerland) · 2026Article
- An Improved Artificial Lemming Algorithm Integrating Non-Uniform Mutation and Q-Learning Adaptation for Underwater Manipulator Controller Tuning.Biomimetics (Basel, Switzerland) · 2026Article
- Multi-Mechanism Artificial Lemming Algorithm for Global Optimization and Color Multi-Threshold Image Segmentation.Biomimetics (Basel, Switzerland) · 2026Article
- EODE-PFA: A Multi-Strategy Enhanced Pathfinder Algorithm for Engineering Optimization and Feature Selection.Biomimetics (Basel, Switzerland) · 2026Article
- MEIAO: A Multi-Strategy Enhanced Information Acquisition Optimizer for Global Optimization and UAV Path Planning.Biomimetics (Basel, Switzerland) · 2025Article
- Research on Robot Obstacle Avoidance and Generalization Methods Based on Fusion Policy Transfer Learning.Biomimetics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
This paper presents an enhanced artificial lemming algorithm (EALA) for solving complex unmanned aircraft system (UAV) path planning problems in three-dimensional environments. Key improvements include chaotic initialization, adaptive perturbation, and hybrid mutation, enabling a better exploration-exploitation balance and local refinement. Validation on the IEEE CEC2017 and CEC2022 benchmark functions demonstrates the EALA's superior performance, achieving faster convergence and better algorithm performance compared to the standard ALA and 10 other algorithms. When applied to UAV path planning in large- and medium-scale environments with realistic obstacle constraints, the EALA generates Pareto-optimal paths that minimize length, curvature, and computation time while guaranteeing collision avoidance. Benchmark tests and realistic simulations show that the EALA outperforms 10 algorithms. This method is particularly suited for mission-critical applications with strict safety and time constraints.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.