Mycelial Search: A Graph-Structured Metaheuristic for Continuous Optimisation
2026-08-24 • Neural and Evolutionary Computing
Neural and Evolutionary ComputingArtificial Intelligence
AI summaryⓘ
The authors propose a new method called Mycelial Search (Myco) for solving continuous optimization problems. Their approach uses a graph where points (solutions) share information within groups (communities) and also between groups, controlled by dynamically adjusted connections. They tested Myco on standard benchmark problems and found it performs well compared to other known methods. Their study shows how controlling information flow and connection strength in this graph affects the search process depending on the problem landscape.
continuous optimizationmetaheuristicgraph-structured searchLouvain partitioncommunity detectionbenchmark suitelocal searchinformation exchangesearch landscape
Authors
Mohammad Mahdi Dehshibi
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.