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December 5, 2025ProcessesOpen Access

A Model-Search Multi-Objective Derivative-Free Optimization Algorithm with Sparse Modeling

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Authors

YLYongxia LiuDSDongying Song

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Overview

The proposed optimization algorithm enhances performance through sparse modeling and linear programming in resource-limited scenarios.

Key Points

  • This algorithm improves optimization processes without gradient information, ensuring efficiency in solution finding.
  • Experimental comparisons on benchmark problems like ZDT and WFG reveal significant performance enhancements and robustness.
  • Leveraging sparse modeling and quadratic surrogate models, this method optimally evaluates candidate solutions under constraints.
  • Using Pareto dominance principles, the algorithm effectively refines selections within a direct multisearch framework.

Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/693231368e51979591dcea06https://doi.org/10.3390/pr13123868
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