| _version_ | 1866902034899271680 |
|---|---|
| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>This paper proposes a novel metaheuristic optimization algorithm based on the behavior of Morph butterflies in rainforests—the Rainforest Butterfly Optimization Algorithm (RMOA). This algorithm simulates the migration, foraging, and color perception behaviors of Morph butterflies in tropical rainforests, organically combining global exploration, local exploitation, and group cooperation to form an optimization strategy with efficient search capabilities and adaptive performance. This paper provides a detailed description of the algorithm modeling, iterative process, mechanism analysis, and mathematical formula derivation, and delves into its multi-modal search capabilities, resistance to local optima, adaptive weight adjustment mechanism, and potential extended applications. Theoretical analysis shows that RMOA possesses significant global search capabilities and convergence stability, providing an effective solution to complex optimization problems</span>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18267095 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Rainforest Morpho Butterfly Optimization Algorithm Zhang, Jincheng <p><span>This paper proposes a novel metaheuristic optimization algorithm based on the behavior of Morph butterflies in rainforests—the Rainforest Butterfly Optimization Algorithm (RMOA). This algorithm simulates the migration, foraging, and color perception behaviors of Morph butterflies in tropical rainforests, organically combining global exploration, local exploitation, and group cooperation to form an optimization strategy with efficient search capabilities and adaptive performance. This paper provides a detailed description of the algorithm modeling, iterative process, mechanism analysis, and mathematical formula derivation, and delves into its multi-modal search capabilities, resistance to local optima, adaptive weight adjustment mechanism, and potential extended applications. Theoretical analysis shows that RMOA possesses significant global search capabilities and convergence stability, providing an effective solution to complex optimization problems</span>.</p> |
| title | Rainforest Morpho Butterfly Optimization Algorithm |
| url | https://doi.org/10.5281/zenodo.18267095 |