Scholarlink

<p>This paper introduces the Enhanced Frilled Lizard Optimizer (EFLO), an improved metaheuristic algorithm based on the recently proposed Frilled Lizard Optimizer. EFLO addresses key limitations, such as premature convergence and limited local exploitation, by incorporating four main enhancements: an adaptive step size sensitive to population diversity, a top-K prey selection mechanism guiding exploration, a dual-vantage tree-climbing strategy for effective exploitation, and a targeted Gaussian local search focused exclusively on optimal solutions. Extensive benchmarking on twelve single-objective IEEE CEC 2022 functions with dimensions (<i>D</i>&#xa0;=&#xa0;10) and higher dimensional settings (<i>D</i>&#xa0;=&#xa0;20) demonstrates EFLO’s superior performance. EFLO achieves the lowest mean errors on eleven functions and consistently ranks first overall, significantly outperforming the original FLO and twenty-one recent and state-of-the-art optimizers. Statistical analyses, including the Wilcoxon signed-rank test, confirm EFLO’s significant superiority when compared to benchmark optimizers and recent state-of-the-art algorithms. Practical applicability is further validated on five engineering design problems, where EFLO consistently yields optimal or near-optimal solutions with significantly reduced variability. Notably, EFLO achieves a 9.9% mass reduction in robot gripper design, a 1.0% improvement in spring weight optimization, and achieves solutions very close to theoretical or global optima in cantilever beam, three-bar truss, and welded beam problems. These results highlight EFLO’s robustness and effectiveness in addressing complex, high-dimensional, and engineering design optimization tasks. The source code of EFLO is publicly available at: <a href="https://www.mathworks.com/matlabcentral/fileexchange/181623-enhanced-frilled-lizard-optimizer-eflo">https://www.mathworks.com/matlabcentral/fileexchange/181623-enhanced-frilled-lizard-optimizer-eflo</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

Enhanced Frilled Lizard Optimizer for Global Optimization and Engineering Design Problems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月10日

查看全文
  • Ali Rodan

摘要

This paper introduces the Enhanced Frilled Lizard Optimizer (EFLO), an improved metaheuristic algorithm based on the recently proposed Frilled Lizard Optimizer. EFLO addresses key limitations, such as premature convergence and limited local exploitation, by incorporating four main enhancements: an adaptive step size sensitive to population diversity, a top-K prey selection mechanism guiding exploration, a dual-vantage tree-climbing strategy for effective exploitation, and a targeted Gaussian local search focused exclusively on optimal solutions. Extensive benchmarking on twelve single-objective IEEE CEC 2022 functions with dimensions (D = 10) and higher dimensional settings (D = 20) demonstrates EFLO’s superior performance. EFLO achieves the lowest mean errors on eleven functions and consistently ranks first overall, significantly outperforming the original FLO and twenty-one recent and state-of-the-art optimizers. Statistical analyses, including the Wilcoxon signed-rank test, confirm EFLO’s significant superiority when compared to benchmark optimizers and recent state-of-the-art algorithms. Practical applicability is further validated on five engineering design problems, where EFLO consistently yields optimal or near-optimal solutions with significantly reduced variability. Notably, EFLO achieves a 9.9% mass reduction in robot gripper design, a 1.0% improvement in spring weight optimization, and achieves solutions very close to theoretical or global optima in cantilever beam, three-bar truss, and welded beam problems. These results highlight EFLO’s robustness and effectiveness in addressing complex, high-dimensional, and engineering design optimization tasks. The source code of EFLO is publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/181623-enhanced-frilled-lizard-optimizer-eflo.