HOA-OBL: hybrid opposition-based hippopotamus optimization framework for efficient UAV task allocation
G Keerthana, R Padmanaban · 2026
Read HOA-OBL: hybrid opposition-based hippopotamus optimization framework for efficient UAV task allocation by G Keerthana, R Padmanaban (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13518960.txt.
Description
G Keerthana and R Padmanaban present HOA-OBL in 2026, a hybrid tool for assigning tasks to drone fleets. Drones serve disaster cleanup work, site checks, and watch duties, yet fixed task plans waste energy and overload some aircraft as conditions change. The base method, Hippopotamus Optimization, mimics hippo herd behavior but can settle too early on mediocre plans.
Opposition-based learning was woven in so the search also probes mirror-image plans, widening the hunt. Tests with 20 drones and 100 tasks cut energy use by roughly 17 to 33 percent versus four rival algorithms. Load spread evened out by about 33 to 50 percent. The pair argue the pairing scales better for real missions.
Catalog record
| CREATOR | G Keerthana, R Padmanaban |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 85332 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |