A robust XGBoost-based multi-objective optimization algorithm for nonlinear truss structures

Abstract

This paper presents MOEA/D-EpDE XGBoost, a novel multi-objective optimization (MOO) algorithm designed for efficient and accurate design optimization of nonlinear inelastic steel truss structures. The algorithm integrates a gradient boosting machine learning model (XGBoost) with a dynamic resource allocation multi-objective evolutionary algorithm (MOEA/D-DRA) and an improved pbest-based Differential Evolution (EpDE) algorithm. XGBoost serves as a surrogate model for computationally expensive finite element analyses (FEA), significantly reducing computational costs while maintaining solution accuracy. The performance of MOEA/D-EpDE XGBoost is compared against five other established MOO algorithms (NSGA2, SPEA2, GDE3, MOEA/D, and a standard ME algorithm) using a 47-bar powerline truss benchmark problem. Results demonstrate that the proposed algorithm achieves superior convergence, diversity, and computational efficiency compared to existing algorithms, while maintaining solution quality.