Solving UAV 3D Path Planning Based on the Improved Lemur Optimizer Algorithm DOI Creative Commons
Haijun Liang, Wenhai Hu,

Gong Ke

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(11), P. 654 - 654

Published: Oct. 25, 2024

This paper proposes an Improved Lemur Optimization algorithm (ILO), which combines the advantages of Spider Monkey algorithm, Simulated Annealing and algorithm. Through use adaptive nonlinear decrement model, learning factors, updated jump rates, enhances its global exploration local exploitation capabilities. A Gaussian function model is used to simulate mountain environment, a mathematical for UAV flight established based on constraints objective functions. The fitness employed determine minimum cost avoiding obstacles in designated airspace, cubic spline interpolation smooth path. was tested using CEC2017 benchmark set, assessing search capability, convergence speed, accuracy. simulation results show that ILO generates high-quality, paths with fewer iterations, overcoming issues premature insufficient ability traditional genetic algorithms. It adapts complex terrain, providing efficient reliable solution.

Language: Английский

Dung beetle optimization with composite population initialization and multi-strategy learning for multi-level threshold image segmentation DOI
Zhidan Li, Wei Liu, Hongying Zhao

et al.

Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(3)

Published: Jan. 28, 2025

Language: Английский

Citations

0
Research on prediction method of reservoir key parameters using deep network architecture based on cross feature fusion with optimization mechanism DOI
Fengcai Huo, Xindi Zhao, Hongli Dong

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: April 8, 2025

Language: Английский

Citations

0
An Improved Spider Wasp Optimizer for UAV Three-Dimensional Path Planning DOI Creative Commons
Haijun Liang, Wenhai Hu,

Lifei Wang

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(12), P. 765 - 765

Published: Dec. 16, 2024

This paper proposes an Improved Spider Wasp Optimizer (ISWO) to address inaccuracies in calculating the population (N) during iterations of SWO algorithm. By innovating iteration formula and integrating advantages Differential Evolution Crayfish Optimization Algorithm, along with introducing opposition-based learning strategy, ISWO accelerates convergence. The adaptive parameters trade-off probability (TR) crossover (Cr) are dynamically updated balance exploration exploitation phases. In each generation, optimizes individual positions using Lévy flights, DE’s mutation, operations, COA’s update mechanisms. OBL strategy is applied every 10 generations enhance diversity. As progress, size gradually decreases, ultimately yielding optimal solution recording convergence process. algorithm’s performance tested 2017 test set, modeling a mountainous environment Gaussian function model. Under constraint conditions, objective establish mathematical model for UAV flight. minimal cost obstacle-avoiding flight within specified airspace obtained fitness function, path smoothed through cubic spline interpolation. Overall, generates high-quality, smooth paths fewer iterations, overcoming premature insufficient local search capabilities traditional genetic algorithms, adapting complex terrains, providing efficient reliable solution.

Language: Английский

Citations

1
Solving UAV 3D Path Planning Based on the Improved Lemur Optimizer Algorithm DOI Creative Commons
Haijun Liang, Wenhai Hu,

Gong Ke

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(11), P. 654 - 654

Published: Oct. 25, 2024

This paper proposes an Improved Lemur Optimization algorithm (ILO), which combines the advantages of Spider Monkey algorithm, Simulated Annealing and algorithm. Through use adaptive nonlinear decrement model, learning factors, updated jump rates, enhances its global exploration local exploitation capabilities. A Gaussian function model is used to simulate mountain environment, a mathematical for UAV flight established based on constraints objective functions. The fitness employed determine minimum cost avoiding obstacles in designated airspace, cubic spline interpolation smooth path. was tested using CEC2017 benchmark set, assessing search capability, convergence speed, accuracy. simulation results show that ILO generates high-quality, paths with fewer iterations, overcoming issues premature insufficient ability traditional genetic algorithms. It adapts complex terrain, providing efficient reliable solution.

Language: Английский

Citations

0