用搜索行为分析比较优化算法，发现114个算法中存在行为相似者。
Comparing Optimization Algorithms Through the Lens of Search Behavior Analysis
- 通过交叉匹配统计检验对比算法搜索轨迹的多维分布
- 在MEALPY库中分析114个算法，识别出具有相似行为的算法群
- 为新算法设计提供行为层面的可比性依据，适合算法评估研究者
数值优化领域近年来涌现出大量受自然或人为过程启发的“新型”元启发式算法，常因概念包装掩盖真正创新，难以与已有方法区分。为回应此问题，本文研究基于搜索行为的算法比较方法，采用交叉匹配统计检验来比较多变量分布，分析来自MEALPY库的114个算法所生成解的分布特征。结果被整合进一项实证研究，旨在识别具有相似搜索行为的算法。该方法为算法性能比较提供了新的行为维度视角。
原文摘要 · Abstract (English)
The field of numerical optimization has recently seen a surge in the development of "novel" metaheuristic algorithms, inspired by metaphors derived from natural or human-made processes, which have been widely criticized for obscuring meaningful innovations and failing to distinguish themselves from existing approaches. Aiming to address these concerns, we investigate the applicability of statistical tests for comparing algorithms based on their search behavior. We utilize the cross-match statistical test to compare multivariate distributions and assess the solutions produced by 114 algorithms from the MEALPY library. These findings are incorporated into an empirical analysis aiming to identify algorithms with similar search behaviors.
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