A competitive coevolution-based evolutionary algorithm for the parallel inverse design of multiple soft network materials

Abstract

Soft network materials (SNMs) incorporating curved microstructures within lattice architectures have emerged as critical components in flexible electronics and tissue scaffolds. These materials demonstrate distinctive nonlinear mechanical behavior under tensile loading, replicating J-shaped stress-strain curves observed in soft tissues such as skin and tendon/ligament. While numerous SNMs featuring diverse microstructural and topological designs have been engineered over the past decade, and each characterized by a distinct design domain, the existing design methodologies present inherent limitations. Although various computational strategies have been proposed, their implementation typically requires multi-stage processes and subjective parameter selection, exhibiting narrow applicability to specific SNM. To address these design constraints, we developed an innovatively competitive coevolution-based differential evolution algorithm with multi-population architecture (CCMPDE). After integrating mathematical modeling of SNMs and finite element analysis (FEA), the CCMPDE-based strategy enabled concurrent optimization of multiple SNMs featuring different curved microstructures. Notably, the proposed strategy demonstrates exceptional compatibility with diverse topologies and mechanical relationships. Through computational and experimental case studies, designed SNMs (fabricated by biopolymers) composed of horseshoe, sinusoidal, and arbitrary curved microstructures successfully replicated the tensile responses of soft tissues. Furthermore, the CCMPDE-based strategy facilitated inverse design of biocomposite SNMs, achieving appropriate replication of target mechanical behaviors.

Keywords

• Soft network materials
• Inverse-engineering design
• Evolutionary algorithm
• Nonlinear mechanical responses
• Biocomposite SNM