| _version_ | 1866901251124363264 |
|---|---|
| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>Swarm optimization algorithms have demonstrated significant performance in continuous optimization, multimodal function solving, and complex constraint problems in recent years, including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Ant Colony Optimization (ACO). This paper proposes a novel swarm optimization algorithm—the Macaw Optimization Algorithm (MOA)—inspired by the foraging behavior, social learning, and environmental alertness of macaw colonies. MOA achieves an effective balance between global search and local fine-grained search by introducing adaptive exploration-development weights, a multi-leader parrot mechanism, dynamic social learning, an alertness mechanism, and an adaptive perturbation refinement strategy. This improves search efficiency and effectively avoids premature convergence. This paper provides a detailed mathematical model of the algorithm's principles and conducts experimental verification on a classic optimization test function. The results show that MOA outperforms traditional algorithms in both convergence speed and global optimum finding capability</span>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18183257 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Macaw Optimization Algorithm Zhang, Jincheng <p><span>Swarm optimization algorithms have demonstrated significant performance in continuous optimization, multimodal function solving, and complex constraint problems in recent years, including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Ant Colony Optimization (ACO). This paper proposes a novel swarm optimization algorithm—the Macaw Optimization Algorithm (MOA)—inspired by the foraging behavior, social learning, and environmental alertness of macaw colonies. MOA achieves an effective balance between global search and local fine-grained search by introducing adaptive exploration-development weights, a multi-leader parrot mechanism, dynamic social learning, an alertness mechanism, and an adaptive perturbation refinement strategy. This improves search efficiency and effectively avoids premature convergence. This paper provides a detailed mathematical model of the algorithm's principles and conducts experimental verification on a classic optimization test function. The results show that MOA outperforms traditional algorithms in both convergence speed and global optimum finding capability</span>.</p> |
| title | Macaw Optimization Algorithm |
| url | https://doi.org/10.5281/zenodo.18183257 |