受新冠病毒机制启发，提出一种高效全局优化算法。
Coronavirus Optimization Algorithm: A Success-History Adaptive Evolutionary Framework with Archive-Assisted Search and Stagnation Recovery for Global Optimization
- 模仿病毒传播机制设计搜索算子，融合精英引导与自适应参数调整。
- 在29个测试函数上综合表现最优，尤其擅长处理组合型函数。
- 适合需要高可靠性与透明性的工程优化场景，如复杂系统设计。
本文提出冠状病毒优化算法（COA），一种受SARS-CoV-2启发的、适用于箱约束连续全局优化的成功历史自适应进化框架。COA不模拟疾病传播过程，而是将选定的冠状病毒机制映射为明确的搜索算子，包括精英引导吸引、试验向量生成、自适应参数变化、停滞恢复及种群规模调度。算法结合反向初始化、当前到最佳变异、二项式交叉、外部档案、成功历史自适应、种群缩减与部分重启策略。在10、30和50维下对29个CEC 2017基准函数进行了评估，对比15种竞争性优化器。结果表明，COA在所有维度上均获得最佳总体弗里德曼排名，尤其在组合函数上表现突出。研究证明，COA是一种紧凑、透明且具有竞争力的自适应进化优化器，但对部分混合函数仍存在局限，需进一步验证其在高维场景下的性能。
原文摘要 · Abstract (English)
This paper proposes the Coronavirus Optimization Algorithm (COA), a SARS-CoV-2-inspired success-history adaptive evolutionary optimizer for box-constrained continuous global optimization. COA does not model disease transmission; instead, it maps selected coronavirus mechanisms to explicit search operators, including elite-guided attraction, trial-vector generation, adaptive parameter variation, stagnation recovery, and population-size scheduling. The algorithm combines opposition-based initialization, current-to-pbest mutation, binomial crossover, an external archive, success-history adaptation, population reduction, and partial restart. COA is evaluated on 29 CEC 2017 benchmark functions at 10, 30, and 50 dimensions against 15 competitive optimizers. Results show that COA achieves the best overall Friedman rank across all dimensions, with particularly strong performance on composition functions. The findings demonstrate that COA is a compact, transparent, and competitive adaptive evolutionary optimizer, while also highlighting limitations on some hybrid functions and the need for further high-dimensional validation.
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