S-CARD-CMSA：一种用于多模态优化的具有密度过滤报告的分数感知候选存档
S-CARD-CMSA: A Score-Aware Candidate Archive with Density-Filtered Reporting for Multimodal Optimization
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中文总结 AI 辅助
针对多模态优化，S-CARD-CMSA基于RS-CMSA-ESII构建，保留其核心机制并引入两个扩展。通过分数感知密度过滤报告规则平衡指标构建解集，开发实验显示其能减少冗余报告并提升相关指标，优化时不依赖真实全局最小位置信息。
中文摘要 AI 辅助
多模态优化旨在单次运行中找到多个全局最优或接近最优解。本文提出了S-CARD-CMSA，这是一个基于带排斥子种群的协方差矩阵自适应进化策略（RS-CMSA-ESII）构建的分数感知候选存档和密度过滤报告框架。该方法是为IEEE CEC 2026多模态优化小生境方法基准测试竞赛而开发的。它保留了RS-CMSA-ESII的采样、协方差自适应、禁忌区域更新、重启和终止机制，并引入了两个保守扩展。一是被动二级候选存档记录重启级最佳候选者而不影响搜索轨迹；二是分数感知密度过滤报告规则通过平衡稳健峰值比率和精度驱动的F1分数来构建最终解集。开发实验表明，密度过滤规则在减少冗余报告的同时保留了中等分数感知规则获得的峰值覆盖率。在更广泛的验证子集中，它保持相同的平均RPR，同时提高了平均精度、F1分数和官方分数导向的平均值。该方法在优化过程中不使用真实全局最小位置信息，仅用于离线开发分析和运行后评分。S-CARD-CMSA的源代码可通过给定链接获取。
英文摘要
Multimodal optimization aims to locate multiple globally optimal or near-optimal solutions in a single run. This paper presents \emph{S-CARD-CMSA}, a score-aware candidate-archive and density-filtered reporting framework built on the covariance matrix self-adaptation evolution strategy with repelling subpopulations (RS-CMSA-ESII). The method is developed for the IEEE CEC 2026 Competition on Benchmarking Niching Methods for Multimodal Optimization. Rather than modifying the core search dynamics of RS-CMSA-ESII, S-CARD-CMSA preserves its sampling, covariance adaptation, taboo-region update, restart, and termination mechanisms. Two conservative extensions are introduced. First, a passive secondary candidate archive records the restart-level best candidates without influencing the search trajectory. Second, a score-aware density-filtered reporting rule constructs the final solution set by balancing robust peak ratio and precision-driven F1-score. Development experiments show that the density-filtered rule preserves the peak coverage obtained by a medium score-aware rule while reducing redundant reports. On a broader validation subset, it maintains the same mean RPR while improving mean precision, F1-score, and the official-score-oriented average. The method does not use true global-minimum locations during optimization; such information is used only for offline development analysis and post-run scoring. The source code of S-CARD-CMSA is available at https://github.com/ChauhanDikshit.
