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<p>Real-world optimization challenges frequently involve computationally expensive evaluations, necessitating efficient optimization strategies. To address the demands of medium-scale expensive optimization problems, this research proposed a novel Surrogate Assisted Evolutionary Algorithm (SAEA): the Weighted Committee-Based Surrogate-Assisted Differential Evolution Framework (WCBDEF). This framework combines principles from active learning and ensemble learning, iteratively interrogating the most ambiguous and high-fidelity solutions to ensure judicious allocation of evaluation resources. WCBDEF employs a dual sampling criterion, with offline optimization dedicated to exploration and online optimization focused on exploitation. Comparison experiments with 4 classical and 4 state-of-the-art SAEAs were conducted on 18 benchmark functions to verify the effectiveness of WCBDEF on medium-sized expensive optimization problems respectively. Moreover, its application in optimizing operational parameters for two Enhanced Geothermal Systems (EGS) models has resulted in a significant reduction in the Levelized Cost of Electricity (LCOE), surpassing existing algorithmic solutions. The results show that WCBDEF is a competitive alternative for medium-scale expensive optimization problems.</p>

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Weighted committee-based surrogate-assisted differential evolution framework for efficient medium-scale expensive optimization

  • 期刊论文

  • 发表日期：

    2025年4月22日

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  • Xiaoqing Ren,
  • Hongliang Wang,
  • Hanyu Hu,
  • Jian Wang,
  • Sergey V. Ablameyko

摘要

Real-world optimization challenges frequently involve computationally expensive evaluations, necessitating efficient optimization strategies. To address the demands of medium-scale expensive optimization problems, this research proposed a novel Surrogate Assisted Evolutionary Algorithm (SAEA): the Weighted Committee-Based Surrogate-Assisted Differential Evolution Framework (WCBDEF). This framework combines principles from active learning and ensemble learning, iteratively interrogating the most ambiguous and high-fidelity solutions to ensure judicious allocation of evaluation resources. WCBDEF employs a dual sampling criterion, with offline optimization dedicated to exploration and online optimization focused on exploitation. Comparison experiments with 4 classical and 4 state-of-the-art SAEAs were conducted on 18 benchmark functions to verify the effectiveness of WCBDEF on medium-sized expensive optimization problems respectively. Moreover, its application in optimizing operational parameters for two Enhanced Geothermal Systems (EGS) models has resulted in a significant reduction in the Levelized Cost of Electricity (LCOE), surpassing existing algorithmic solutions. The results show that WCBDEF is a competitive alternative for medium-scale expensive optimization problems.