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An accelerated method for solving constrained multi-objective optimization | ||
| Journal of Mathematical Modeling | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 09 آذر 1404 اصل مقاله (921.14 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2025.30721.2753 | ||
| نویسندگان | ||
| Niloofar Salehi Mokari1؛ Hadi Basirzadeh* 1؛ Vahid Morovati2 | ||
| 1Department of Mathematics, Faculty of Mathematical Sciences and Computer, Shahid Chamran University of Ahvaz, Ahvaz, Iran | ||
| 2Department of Mathematics, University of Hormozgan, Bandarabbas, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Constrained Multi-objective Optimization Problems؛ Feasible Direction Methods؛ Line-Search Techniques؛ Pareto Critical Point | ||
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آمار تعداد مشاهده مقاله: 8 تعداد دریافت فایل اصل مقاله: 13 |
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