Fishes Go MOO: Pareto analysis for speed and cost of transport across a 6-dimensional design space

Aquatic organisms exhibit remarkable diversity in swimming strategies, even within shared modes such as body–caudal fin (BCF) propulsion. Here, we investigate the biomechanical underpinnings of BCF swimming by mapping performance trade-offs across a 6-dimensional design space. Using a computational framework that integrates computational fluid dynamics (CFD), machine learning (ML), multi-objective optimization (MOO), and global sensitivity analysis (GSA), we identified distinct Pareto-optimal fronts between swimming speed and cost of transport. Along these fronts, we uncovered key performance relationships, including that propulsive efficiency is maximized when speed and cost of transport are weighted nearly equally in the objective function, highlighting the benefits of balancing competing demands. We further demonstrate that multiple combinations of kinematic traits can yield comparable performance, revealing both redundancies and sensitivities that provide a mechanistic basis for the diversity of swimming patterns observed in fish. Together, these results generate new biological hypotheses and suggest how evolutionary pressures may shape locomotor design.