Scholarlink

<p>Uncertain optimization problems play a crucial role in fields that involve decision-making under uncertainty, such as finance, supply chain management, energy systems, healthcare, transportation, engineering design, risk management, telecommunications, and agriculture. This paper develops a trust region method for uncertain multiobjective optimization problems (UMOPs). To find the solution of UMOP, an objective-wise worst-case-type robust counterpart (OWRC) is considered, which transforms the UMOP into a deterministic multiobjective optimization problem (MOP). To solve the OWRC, a trust region algorithm is developed, and the global convergence of this algorithm is also presented. After that, the trust region algorithm is compared with existing methods (e.g., steepest descent method, Newton’s method, modified quasi-Newton method, weighted sum method) for UMOP. The algorithm’s effectiveness is validated through numerical test problems using performance profiles.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A trust region method for uncertain multiobjective optimization: comparative analysis with existing descent methods

  • 期刊论文

  • 发表日期：

    2026年1月27日

查看全文
  • Shubham Kumar,
  • Nihar Kumar Mahato,
  • Debdas Ghosh

摘要

Uncertain optimization problems play a crucial role in fields that involve decision-making under uncertainty, such as finance, supply chain management, energy systems, healthcare, transportation, engineering design, risk management, telecommunications, and agriculture. This paper develops a trust region method for uncertain multiobjective optimization problems (UMOPs). To find the solution of UMOP, an objective-wise worst-case-type robust counterpart (OWRC) is considered, which transforms the UMOP into a deterministic multiobjective optimization problem (MOP). To solve the OWRC, a trust region algorithm is developed, and the global convergence of this algorithm is also presented. After that, the trust region algorithm is compared with existing methods (e.g., steepest descent method, Newton’s method, modified quasi-Newton method, weighted sum method) for UMOP. The algorithm’s effectiveness is validated through numerical test problems using performance profiles.