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

Malik Mushthofa; John Thedy; Han Ay Lie; Purwanto; Marc Ottelé; Mochamad Teguh

doi:10.46604/ijeti.2026.15905

Authors

Malik Mushthofa Department of Civil Engineering, Diponegoro University, Semarang, Indonesia/ Faculty of Civil Engineering and Planning, Islamic University of Indonesia, Yogyakarta, Indonesia
John Thedy Department of Civil Engineering, Diponegoro University, Semarang, Indonesia/ Formerly Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan, ROC https://orcid.org/0009-0008-6016-6770
Han Ay Lie Department of Civil Engineering, Diponegoro University, Semarang, Indonesia https://orcid.org/0000-0002-0990-5274
Purwanto Department of Civil Engineering, Diponegoro University, Semarang, Indonesia https://orcid.org/0000-0002-1096-8088
Marc Ottelé Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, Netherlands
Mochamad Teguh Faculty of Civil Engineering and Planning, Islamic University of Indonesia, Yogyakarta, Indonesia https://orcid.org/0000-0003-2709-3975

DOI:

https://doi.org/10.46604/ijeti.2026.15905

Keywords:

multi-target optimization, mix design optimization, particle swarm optimization, surrogate modeling, gradient boosting

Abstract

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.

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