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<p>This paper introduces dynamic boundary updating-surrogate model-based (DBU-SMB), a novel evolutionary framework for global optimization that integrates dynamic boundary updating (DBU) within a surrogate model-based (SMB) approach. The method operates in three progressive stages: adaptive sampling, DBU, and refinement. In the first stage, adaptive sampling strategically explores the design space to gather critical information for improving the surrogate model. The second stage incorporates DBU to guide the optimization toward promising regions in the parameter space, enhancing consistency and efficiency. Finally, the refinement stage iteratively improves the optimization results, ensuring a comprehensive exploration of the design space. The proposed DBU-SMB framework is algorithm-agnostic, meaning it does not rely on any specific machine learning model or meta-heuristic algorithm. To demonstrate its effectiveness, we applied DBU-SMB to four highly nonlinear and non-convex optimization problems. The results show a reduction of over 90% in the number of function evaluations compared to traditional methods, while avoiding entrapment in local optima and discovering superior solutions. These findings highlight the efficiency and robustness of DBU-SMB in achieving optimal designs, particularly for large-scale and complex optimization problems.</p>

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Adaptive surrogate-based optimization with dynamic boundary updating for structural problems

  • 期刊论文

  • 发表日期：

    2025年8月8日

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  • Majid Ilchi Ghazaan,
  • Mostafa Sharifi

摘要

This paper introduces dynamic boundary updating-surrogate model-based (DBU-SMB), a novel evolutionary framework for global optimization that integrates dynamic boundary updating (DBU) within a surrogate model-based (SMB) approach. The method operates in three progressive stages: adaptive sampling, DBU, and refinement. In the first stage, adaptive sampling strategically explores the design space to gather critical information for improving the surrogate model. The second stage incorporates DBU to guide the optimization toward promising regions in the parameter space, enhancing consistency and efficiency. Finally, the refinement stage iteratively improves the optimization results, ensuring a comprehensive exploration of the design space. The proposed DBU-SMB framework is algorithm-agnostic, meaning it does not rely on any specific machine learning model or meta-heuristic algorithm. To demonstrate its effectiveness, we applied DBU-SMB to four highly nonlinear and non-convex optimization problems. The results show a reduction of over 90% in the number of function evaluations compared to traditional methods, while avoiding entrapment in local optima and discovering superior solutions. These findings highlight the efficiency and robustness of DBU-SMB in achieving optimal designs, particularly for large-scale and complex optimization problems.