Aircraft Conceptual Design for Cloud Seeding: A Comparative Study of Recent Many-Objective Metaheuristics
Issued Date
2026-02-01
Resource Type
eISSN
22264310
Scopus ID
2-s2.0-105031496849
Journal Title
Aerospace
Volume
13
Issue
2
Rights Holder(s)
SCOPUS
Bibliographic Citation
Aerospace Vol.13 No.2 (2026)
Suggested Citation
Champasak P., Kunakorn-ong P., Kanokmedhakul Y., Bureerat S., Pholdee N., Panagant N. Aircraft Conceptual Design for Cloud Seeding: A Comparative Study of Recent Many-Objective Metaheuristics. Aerospace Vol.13 No.2 (2026). doi:10.3390/aerospace13020202 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115599
Title
Aircraft Conceptual Design for Cloud Seeding: A Comparative Study of Recent Many-Objective Metaheuristics
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Water scarcity and increasing climate variability have strengthened the demand for effective weather-modification technologies such as cloud seeding. In Thailand, conventional manned rainmaking aircraft remain constrained by operational range, safety risks, and sustainability considerations, motivating the development of electric vertical take-off and landing unmanned aerial vehicles (eVTOL-UAVs). This paper proposes a mission-driven conceptual design and optimization framework for a cloud-seeding eVTOL-UAV, and extends it to reliability-based design optimization (RBDO) under uncertainty. The design task is formulated as a five-objective many-objective optimization problem with the following objectives: minimizing take-off weight, turn radius, and probability of failure, while maximizing endurance and climb rate, subject to stability/control and performance constraints. Ten state-of-the-art many-objective metaheuristics are benchmarked and solve the problem, and their performance is assessed using hypervolume (HV), inverted generational distance (IGD), runtime, and Friedman rank statistics. Results show that AGEMOEAII and PREA consistently provide the most competitive solution-set quality (HV/IGD) with comparable computational cost across algorithms. A deterministic–reliability comparison further demonstrates a clear robustness gap. Five representative Pareto designs from the best-performing optimizer are reported to illustrate practical trade-offs and support decision-making for sustainable, autonomous cloud-seeding operations.