An adaptive multi-strategy metaheuristic for robust model calibration in large-scale systems biology

Abstract

Parameter estimation is a central challenge in systems biology, particularly for large dynamic models described by nonlinear ordinary differential equations (ODEs). These global optimization problems exhibit landscapes which are topologically heterogeneous, often exhibiting a pathological mixture of stiff, smooth valleys and rugged, noisy plateaus, making single-strategy hybrids ineffective. While methods like enhanced Scatter Search (eSS) represent the current state-of-the-art, their rigid intensification strategies can limit performance in large-scale or ill-conditioned scenarios. In this work, we introduce eLSHADE+, a novel metaheuristic architecture designed to adapt to these topological challenges. The proposed algorithm augments a recent Differential Evolution variant (LSHADE) with a probabilistic multistrategy hybridization. Unlike standard memetic algorithms, eLSHADE+ uses three distinct operational modes: (i) gradient-based intensification for precision in differentiable regions, (ii) derivative-free search for robustness against numerical noise, and (iii) pure global exploration to conserve computational resources in complicated basins. Additionally, a logarithmic parameter space transformation is incorporated to facilitate search across multi-scale biological constants. We rigorously evaluated eLSHADE+ using the BioPreDyn benchmark suite, comprising challenging real-world problems. Comparative analysis demonstrates that this adaptive multi-strategy approach yields statistically superior convergence speed and solution accuracy compared to eSS and other competitive metaheuristics, establishing a new baseline for robust model calibration in computational biology. Code and data are available at https://doi.org/10.5281/zenodo.18379327.