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<p>Surrogate-assisted evolutionary algorithms are widely used for solving expensive and complex optimization problems, yet existing methods face persistent challenges in balancing accuracy and efficiency, particularly in high-dimensional spaces. This paper presents SRIME, an advanced surrogate-assisted RIME algorithm that introduces three key innovations to overcome these limitations. First, our elite sample sampling mechanism generates high-quality initial populations while maintaining computational efficiency. Second, we develop an improved Gaussian regression model that dynamically incorporates historical optimal data during optimization for more accurate function approximation. Most significantly, we propose two novel strategies with theoretical guarantees: (1) an adaptive long-distance dynamic search that automatically adjusts exploration ranges based on population distribution to prevent premature convergence, and (2) a mutation-assisted local search that provides efficient refinement in promising regions. Comprehensive theoretical analysis demonstrates SRIME's convergence properties and computational complexity advantages, Statistical tests confirm these improvements are significant (p &lt; 0.01). We further validate SRIME's practical utility through two important applications. In photovoltaic parameter estimation, SRIME achieves highly accuracy under challenging outdoor conditions, showing particular robustness to temperature and light variations. These results demonstrate SRIME's effectiveness in both synthetic benchmarks and real-world optimization problems, with special strengths in handling high-dimensional scenarios that challenge existing methods.</p>

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Surrogate-assisted enhanced RIME algorithm for high-dimensional feature selection and complex optimization problems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月31日

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  • Huangying Wu,
  • Qiuyu Lee,
  • Zhennao Cai,
  • Chengxuan Chen,
  • Qian Zhang,
  • Huiling Chen

摘要

Surrogate-assisted evolutionary algorithms are widely used for solving expensive and complex optimization problems, yet existing methods face persistent challenges in balancing accuracy and efficiency, particularly in high-dimensional spaces. This paper presents SRIME, an advanced surrogate-assisted RIME algorithm that introduces three key innovations to overcome these limitations. First, our elite sample sampling mechanism generates high-quality initial populations while maintaining computational efficiency. Second, we develop an improved Gaussian regression model that dynamically incorporates historical optimal data during optimization for more accurate function approximation. Most significantly, we propose two novel strategies with theoretical guarantees: (1) an adaptive long-distance dynamic search that automatically adjusts exploration ranges based on population distribution to prevent premature convergence, and (2) a mutation-assisted local search that provides efficient refinement in promising regions. Comprehensive theoretical analysis demonstrates SRIME's convergence properties and computational complexity advantages, Statistical tests confirm these improvements are significant (p < 0.01). We further validate SRIME's practical utility through two important applications. In photovoltaic parameter estimation, SRIME achieves highly accuracy under challenging outdoor conditions, showing particular robustness to temperature and light variations. These results demonstrate SRIME's effectiveness in both synthetic benchmarks and real-world optimization problems, with special strengths in handling high-dimensional scenarios that challenge existing methods.