This paper presents a simulation-driven framework integrating the Multi-Objective Scientific Approach to Problem Solving-inspired Optimization (MOSAPSO) algorithm with the finite element method (FEM) for automated structural design in construction. The proposed MOSAPSO integrates chaotic initialization, Lévy flight dynamics, elite population control, and sparsity-biased Pareto archiving to enhance convergence and diversity, while embedding the scientific research process, including review and problem definition, hypothesis formulation, data collection, and analysis and interpretation, into a unified optimization strategy. A temporal control strategy balances exploration and exploitation during the optimization process. Benchmarking on 24 CEC-2020 test functions reveals that MOSAPSO outperforms 11 established multi-objective algorithms across hypervolume (HV), generational distance (GD), and spacing (SP) metrics. Integrated with FEM, MOSAPSO–FEM automatically generates Pareto-optimal designs for five large-scale structural systems, balancing weight, displacement, and stability constraints. The framework provides a robust foundation for intelligent, simulation-driven decision-making in construction design, offering significant opportunities for integration with BIM, digital twins, and automated design tools. • Develops MOSAPSO, a nonlinear dynamics–inspired multiobjective framework for automated design optimization. • Integrates chaotic mapping, Lévy flights, elite selection, and sparsity-biased Pareto archiving to enhance search performance. • Achieves superior convergence and diversity on CEC-2020 benchmark functions against state-of-the-art algorithms. • Combines MOSAPSO with FEM to enable simulation-driven and automation-oriented structural design. • Provides an intelligent, automation-ready tool for multiobjective decision support in construction.
Truong et al. (Tue,) studied this question.