Mycelial Search: A Graph-Structured Metaheuristic for Continuous Optimisation

Abstract: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.

Submitted to "Expert Systems With Applications"; 18 pages, 6 figures, 6 tables. To facilitate reproducibility, the Python implementation of Mycelial Search (Myco) is publicly available on [GitHub](this https URL)

Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)

arXiv:2608.23323 [cs.NE]

(or arXiv:2608.23323v1 [cs.NE] for this version)

https://doi.org/10.48550/arXiv.2608.23323 arXiv-issued DOI via DataCite (pending registration)