University of Guilan Journal of Mathematical Modeling 2345-394X 14 2 2026 05 01 An accelerated method for solving constrained multi-objective optimization 509 532 9240 10.22124/jmm.2025.30721.2753 EN Niloofar Salehi Mokari Department of Mathematics, Faculty of Mathematical Sciences and Computer, Shahid Chamran University of Ahvaz, Ahvaz, Iran Hadi Basirzadeh Department of Mathematics, Faculty of Mathematical Sciences and Computer, Shahid Chamran University of Ahvaz, Ahvaz, Iran Vahid Morovati Department of Mathematics, University of Hormozgan, Bandarabbas, Iran Journal Article 2025 05 20 A novel non-parametric algorithm is introduced for solving constrained multi-objective optimization problems. At each iteration, a convex sub-problem is solved to determine the search direction, while a non-monotone line search technique is used to determine the step size. An adaptive acceleration term, computed from changes in the search directions, is incorporated to scale the step and dynamically enhance convergence performance. The algorithm’s effectiveness relies on a diverse set of initial feasible solutions to accurately approximate the non-dominated boundary. Benchmark tests validate the approach, with Pareto fronts compared to those obtained using the Zoutendijk method. Numerical evaluations demonstrate superior performance in terms of convergence rate and solution quality. The algorithm is also applied to a real-world engineering design problem involving speed reduction, highlighting its computational efficiency and robustness in practical applications. https://jmm.guilan.ac.ir/article_9240_6325aaf7c4dccebd2cd9ddacf487bb28.pdf