Single and Multi-Objective Benchmark Problems Focusing on Human-Powered Aircraft Design

This paper introduces a novel set of benchmark problems aimed at advancing research in both single and multi-objective optimization, with a specific focus on the design of human-powered aircraft (HPA). These benchmark problems are unique in that they incorporate real-world design considerations such as fluid dynamics and material mechanics, providing a more realistic simulation of engineering design optimization. We propose three difficulty levels and a wing segmentation parameter in these problems, allowing for scalable complexity to suit various research needs. The problems are designed to be computationally reasonable, ensuring short evaluation times, while still capturing the moderate multimodality of engineering design problems. Our extensive experiments using popular evolutionary algorithms for multi-objective problems demonstrate that the proposed benchmarks effectively replicate the diverse Pareto front shapes observed in real-world problems, including convex, linear, concave, and degenerated forms. The benchmarks and their Python source codes are made publicly available for broader use in the optimization research community.