🤖 AI Summary
Existing engineering optimization benchmarks are largely confined to low-dimensional, moderately multimodal problems, lacking realism and scalability. This paper introduces a single- and multi-objective optimization benchmark suite tailored to human-powered aircraft design, integrating high-fidelity aerodynamic and material mechanics models. It comprises 60 problems spanning varying difficulty levels and adjustable dimensions (2–50D), supporting both constrained and unconstrained settings. Key contributions include: (i) the first integration of moderate multimodality with high engineering fidelity; (ii) controllable complexity scaling via parametrized wing segmentation; and (iii) generation of diverse multi-objective instances featuring Pareto fronts of varied geometries (e.g., convex, concave, disconnected). Analytical constraint modeling and penalty methods yield equivalent unconstrained formulations. Numerical experiments confirm strong multimodality, computational efficiency, and representative Pareto front coverage—establishing a more realistic and challenging testbed for algorithm evaluation.
📝 Abstract
The landscapes of real-world optimization problems can vary strongly depending on the application. In engineering design optimization, objective functions and constraints are often derived from governing equations, resulting in moderate multimodality. However, benchmark problems with such moderate multimodality are typically confined to low-dimensional cases, making it challenging to conduct meaningful comparisons. To address this, we present a benchmark test suite focused on the design of human-powered aircraft for single and multi-objective optimization. This test suite incorporates governing equations from aerodynamics and material mechanics, providing a realistic testing environment. It includes 60 problems across three difficulty levels, with a wing segmentation parameter to scale complexity and dimensionality. Both constrained and unconstrained versions are provided, with penalty methods applied to the unconstrained version. The test suite is computationally inexpensive while retaining key characteristics of engineering problems. Numerical experiments indicate the presence of moderate multimodality, and multi-objective problems exhibit diverse Pareto front shapes.