发表机构
Unconventional Computing Laboratory, UWE Bristol（西英格兰大学布里斯托尔非常规计算实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出图结构元启发式算法 Myco，在 CEC 2022 基准测试中与 11 种优化器对比，验证其在连续优化上的竞争力，揭示社区结构与索带可塑性对算法性能的作用。
AI 中文摘要
连续优化方法需要在信息共享与保留备选搜索方向之间取得平衡。本文提出菌丝体搜索（Mycelial Search，Myco），一种围绕活跃尖端、社区加权流、自适应索带可塑性及锚点注入机制设计的图结构元启发式算法。候选解构成演化空间图，其中 Louvain 划分区分社区内与跨社区的信息交换；自适应索带可塑性随后根据活跃尖端间边与局部流的对齐情况对其进行修改；锚点注入机制补充图驱动的尖端动力学。我们在 CEC 2022 单目标有界约束基准测试套件上对 Myco 进行评估，维度取 $D=10$ 和 $D=20$，每算法-函数对进行 30 次独立运行，对比对象包含来自多个搜索家族的 11 种成熟优化器。Myco 在两个维度的选定函数上取得了具有竞争力的结果； ablation 分析进一步表明，社区结构调节图基信息交换的范围，而索带可塑性控制局部方向影响的持续性。这些发现说明，图结构的局部交互可支持连续优化，其有效性取决于优化景观结构与跨局部搜索区域的信息传递。
英文摘要
Continuous optimisation methods need to balance sharing information and maintaining alternative search directions. In this paper, we introduce Mycelial Search (Myco), a graph-structured metaheuristic designed around active tips, community-weighted flow, adaptive cord plasticity, and anchor-based injection. Candidate solutions form an evolving spatial graph in which a Louvain partition distinguishes within-community from cross-community information exchange. Adaptive cord plasticity subsequently modifies active tip-to-tip edges according to their alignment with the local flow. An anchor-based injection mechanism supplements the graph-driven tip dynamics. We evaluated Myco on the CEC 2022 single-objective bound-constrained benchmark suite at dimensions $D=10$ and $D=20$, using 30 independent runs per algorithm-function pair. The comparison includes eleven established optimisers from several search families. Myco reaches competitive results on selected functions across both dimensions. The ablation analysis further shows that community structure regulates the range of graph-based information exchange, whereas cord plasticity controls the persistence of local directional influence. These findings indicate that graph-structured local interaction can support continuous optimisation, while its effectiveness depends on landscape structure and information transfer across local search regions.
CommentsThe manuscript contains 18 pages, 6 figures, and 6 tables. To facilitate reproducibility, the Python implementation of Mycelial Search (Myco) is publicly available at: https://github.com/dehshibi/Mycelia-Search
