冠状病毒优化算法：一种带存档辅助搜索和停滞恢复的成功历史自适应进化框架，用于全局优化

中文摘要 AI 辅助

本文提出冠状病毒优化算法（Coronavirus Optimization Algorithm, COA），这是一种受SARS-CoV-2启发的成功历史自适应进化优化器，用于求解带边界约束的连续全局优化问题。COA不模拟疾病传播，而是将选定的冠状病毒机制映射为显式搜索算子，包括精英引导吸引、试验向量生成、自适应参数调整、停滞恢复和种群规模调度。该算法结合了基于对立的初始化、current-to-pbest变异、二项式交叉、外部存档、成功历史自适应、种群缩减和部分重启。在10、30和50维下，针对15种竞争性优化器，在29个CEC 2017基准函数上对COA进行评估。结果表明，COA在所有维度上均取得最佳整体Friedman秩，尤其在组合函数上表现强劲。研究结果表明，COA是一种紧凑、透明且具有竞争力的自适应进化优化器，同时也凸显了其在部分混合函数上存在局限性，需进一步开展高维验证。

英文摘要

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.

发表机构

• Bournemouth University（伯恩茅斯大学）

机构由 AI 辅助整理，请以论文原文为准。