@article{YU2025, 
author = {Mingyang YU and Ting LI and Jing XU},
title = {Enhanced dwarf mongoose optimization algorithm with multi-strategy fusion},
year = {2025},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {51},
number = {11},
pages = {3991-4002},
keywords = {dwarf mongoose optimization algorithm, multi-strategy fusion, random reverse learning, adaptive, fungal foraging behavior, three-dimensional path planning},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2023.0613},
doi = {10.13700/j.bh.1001-5965.2023.0613},
abstract = {The enhanced multi-strategy dwarf mongoose optimization algorithm (EDMO) is a proposed solution to the dwarf mongoose optimization algorithm's (DMO) low convergence efficiency and susceptibility to local optima. This algorithm employs a random opposite learning strategy to amplify the diversity and quality of the mongoose population, bolstering its global search capability and enhancing convergence accuracy. Concurrently, an adaptive approach is deployed to update the babysitter exchange coefficient, striking a balance between global exploration and local exploitation. In the latter stages of iteration, the algorithm capitalizes on the foraging behavior of the slime mold, optimizing between local and global optimal solutions. By solving the CEC2017 test function set, different algorithms are compared. The findings demonstrate that in terms of optimization accuracy, optimization speed, and resilience, EDMO which combines the three strategies performs better than the sophisticated algorithms under comparison. Through the experimental verification of UAV three-dimensional path planning, the EDMO algorithm performs better than the original DMO algorithm in local search, and the flight path generated is more stable.}
}