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Numerical results for the multiobjective trust region algorithm MHT.

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This summary is machine-generated.

This study presents numerical data for a trust region algorithm designed for complex optimization problems with costly simulations. The findings support the method

Keywords:
Heterogeneous optimizationMultiobjective optimizationTest problemsTest setTrust region algorithm

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Area of Science:

  • Optimization Theory
  • Numerical Analysis
  • Computational Science

Background:

  • Multiobjective optimization problems often involve computationally expensive functions, such as simulations.
  • Existing algorithms may struggle with heterogeneous objectives where one function is significantly more time-consuming than others.

Purpose of the Study:

  • To provide comprehensive numerical data and results for the MHT trust region algorithm.
  • To evaluate the performance of the MHT algorithm on heterogeneous multiobjective optimization problems with expensive black-box functions.

Main Methods:

  • The study reports data from numerical tests involving 78 diverse test problems.
  • Expensive black-box functions were artificially introduced into test problems to simulate real-world scenarios.

Main Results:

  • The data article details the numerical outcomes of applying the MHT algorithm to a wide range of test instances.
  • Results demonstrate the algorithm's behavior and effectiveness when dealing with the computational cost of black-box functions.

Conclusions:

  • The reported data serves as a valuable resource for researchers in multiobjective optimization.
  • The numerical results support the efficacy of the MHT trust region algorithm for heterogeneous problems involving expensive functions.