Multi-Target Particle Swarm Optimization with Machine Learning Surrogates for Efficient Concrete Mix Design

This study presents a multi-target particle swarm optimization (MT-PSO) approach for efficient concrete mix design. It simultaneously designs mixes with multiple predefined strengths under a constant water-cement ratio. A gradient boosting-based surrogate model, trained on experimental mix data, predicts compressive strength. The modified particle swarm optimization (PSO) algorithm accommodates multiple targets in parallel, allowing solution sharing across target groups. MT-PSO is compared with a repeated PSO (R-PSO) strategy that optimizes each target separately, both minimizing the absolute error between predicted and desired strengths. Across 30 independent trials, MT-PSO consistently achieves lower mean errors, smaller deviations, and faster convergence, often reaching R-PSO’s final accuracy within only a few iterations. Moreover, MT-PSO requires over 85% fewer fitness evaluations. These results demonstrate the superior accuracy, robustness, and computational efficiency of MT-PSO for multi-target optimization problems.