连续优化的基于阈值的选择：一种叶脱落实例化
Threshold-Based Selection for Continuous Optimization: A Leaf-Abscission Instantiation
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中文总结 AI 辅助
本文提出基于阈值选择的评估门控架构，实例化为叶脱落优化（LAO），通过因子分析简化核心，在CEC 2017上取得第三佳排名，证明其作为简约机制的条件性竞争力。
中文摘要 AI 辅助
本文形式化了基于阈值的选择作为一种评估门控架构，其中每个当前解在变异前被测试，仅当情境压力超过内在强度时，才生成并评估替代解。该机制被实例化为叶脱落优化（LAO），采用基于秩的强度、物候季节信号、多样性调制、环境压力以及基础再生长核。在CEC 2017基准套件上，维度为10的区组二的四次幂因子分析将原始多层设计简化为简约核心：漂移有害，而其他三个辅助层未显示出稳健的独立有益证据。由此产生的LAO-Core在相等评估预算下，于维度10、30和50的九个优化器中取得了第三佳的均值弗里德曼秩。四次预算扫描显示性能随预算变化，自适应差分进化基线在较大预算下获得相对优势；九单元维度-预算分析既未确立仅每维度评估预算的规律，也未发现统计显著的维度-预算交互作用。配对干预表明，多样性调制改变了后期运行中的替换行为，但在测试预算下对最终误差无显著影响。证据支持LAO作为一种简约的评估门控机制，具有条件性的竞争力，而非普遍优越的优化器。
英文摘要
This paper formalizes threshold-based selection as an evaluation-gating architecture in which each incumbent is tested before variation and a replacement is generated and evaluated only when contextual pressure exceeds intrinsic strength. The mechanism is instantiated as Leaf Abscission Optimization (LAO), using rank-based strength, a phenological seasonal signal, diversity modulation, environmental pressure, and a base regrowth kernel. A blocked two-to-the-fourth-power factorial analysis at dimension 10 on the CEC 2017 suite reduces the original multi-layer design to a parsimonious core: drift is harmful, while the other three auxiliary layers show no robust independent evidence of benefit. The resulting LAO-Core attains the third-best mean Friedman rank among nine optimizers at dimensions 10, 30, and 50 under the equal evaluation budget. A four-budget sweep shows budget-dependent relative performance, with adaptive differential-evolution baselines gaining relative advantage at larger budgets; the nine-cell dimension-budget analysis establishes neither an evaluation-budget-per-dimension-only law nor a statistically significant dimension-budget interaction. A paired intervention shows that diversity modulation changes late-run replacement behaviour without a detectable effect on final error at the tested budget. The evidence supports LAO as a parsimonious evaluation-gating mechanism with regime-qualified competitiveness, rather than as a generally superior optimizer.
发表机构
- Graduate School of Management and Economics, Sharif University of Technology（谢里夫理工大学管理与经济研究生院）
机构由 AI 辅助整理，请以论文原文为准。
