Scholarlink

<p>As engineering optimization become increasingly complex, researchers seek algorithms with higher performance and self-regulation capabilities. This study proposes a novel physics-inspired metaheuristic algorithm named Forest Fire Ash Optimizer (FFA). Inspired by the movement patterns of forest fire ash, transformed physical laws into multiple algorithmic mechanisms, including a four-population division rule, an inverse proportionality roulette selection mechanism, and an ash movement strategy. The reliability of FFA structure, mechanisms, and parameter combinations is validated on the CEC-2017 and CEC-2022 test suites. Compared with nine advanced algorithms in terms of performance, FFA achieves Friedman’s average ranks of 2.04, 2.67, 2.58, 2.08, and 2.42, ranking first. And the W|T|L against top baselines is |69|0|45|, confirming its superiority and robustness. Finally, FFA is applied to seven engineering optimization problems and wireless sensor networks coverage optimization. The results indicate that, FFA provides superior and novel solutions, predicting its potential for in-depth applications in more engineering fields.</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

Forest fire ash optimizer: a novel physics-based metaheuristic algorithm for implementing exploration–exploitation flexible regulation and its engineering applications

  • 期刊论文

  • 发表日期：

    2026年2月16日

查看全文
  • Baisen Lin,
  • Congzhen Xie,
  • Song Yu,
  • Jigang Wang,
  • Rui Wang

摘要

As engineering optimization become increasingly complex, researchers seek algorithms with higher performance and self-regulation capabilities. This study proposes a novel physics-inspired metaheuristic algorithm named Forest Fire Ash Optimizer (FFA). Inspired by the movement patterns of forest fire ash, transformed physical laws into multiple algorithmic mechanisms, including a four-population division rule, an inverse proportionality roulette selection mechanism, and an ash movement strategy. The reliability of FFA structure, mechanisms, and parameter combinations is validated on the CEC-2017 and CEC-2022 test suites. Compared with nine advanced algorithms in terms of performance, FFA achieves Friedman’s average ranks of 2.04, 2.67, 2.58, 2.08, and 2.42, ranking first. And the W|T|L against top baselines is |69|0|45|, confirming its superiority and robustness. Finally, FFA is applied to seven engineering optimization problems and wireless sensor networks coverage optimization. The results indicate that, FFA provides superior and novel solutions, predicting its potential for in-depth applications in more engineering fields.