Scholarlink

<p>This paper presents the enzyme action optimization (EAO) algorithm, a novel bio-inspired optimization algorithm designed to simulate the adaptive enzyme mechanism in biological systems. EAO employs a novel strategy that dynamically balances between exploration and exploitation to efficiently navigate and optimize complex, multi-dimensional search spaces. EAO has been tested over diverse benchmark datasets, including the 23 classical benchmark functions, IEEE CEC2017, CEC2022 benchmark functions, where it has been compared with 14 recent and highly cited optimizers. The results show the superior performance of EAO over the compared optimizers in terms of finding the optimal solution, convergence speed, robustness, and overall performance. Furthermore, EAO was applied to solve five engineering design problems and demonstrated excellent performance results. The source code of EAO is publicly available for both MATLAB at: <a href="https://www.mathworks.com/matlabcentral/fileexchange/170296-enzyme-action-optimizer-a-novel-bio-inspired-optimization">https://www.mathworks.com/matlabcentral/fileexchange/170296-enzyme-action-optimizer-a-novel-bio-inspired-optimization</a> and PYTHON at: <a href="https://github.com/AliRodan/Enzyme-Action-Optimizer">https://github.com/AliRodan/Enzyme-Action-Optimizer</a>.</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

Enzyme action optimizer: a novel bio-inspired optimization algorithm

  • 期刊论文

  • 发表日期：

    2025年4月1日

查看全文
  • Ali Rodan,
  • Abdel-Karim Al-Tamimi,
  • Loai Al-Alnemer,
  • Seyedali Mirjalili,
  • Peter Tiňo

摘要

This paper presents the enzyme action optimization (EAO) algorithm, a novel bio-inspired optimization algorithm designed to simulate the adaptive enzyme mechanism in biological systems. EAO employs a novel strategy that dynamically balances between exploration and exploitation to efficiently navigate and optimize complex, multi-dimensional search spaces. EAO has been tested over diverse benchmark datasets, including the 23 classical benchmark functions, IEEE CEC2017, CEC2022 benchmark functions, where it has been compared with 14 recent and highly cited optimizers. The results show the superior performance of EAO over the compared optimizers in terms of finding the optimal solution, convergence speed, robustness, and overall performance. Furthermore, EAO was applied to solve five engineering design problems and demonstrated excellent performance results. The source code of EAO is publicly available for both MATLAB at: https://www.mathworks.com/matlabcentral/fileexchange/170296-enzyme-action-optimizer-a-novel-bio-inspired-optimization and PYTHON at: https://github.com/AliRodan/Enzyme-Action-Optimizer.