| _version_ | 1866901939599441920 |
|---|---|
| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>To address the problems of premature convergence, fixed search paths, and over-reliance on local optima in traditional swarm intelligence optimization algorithms for complex, multimodal, and noisy optimization problems, this paper proposes a novel swarm intelligence optimization method—the Blue Tit Optimization Algorithm (BTOA). Inspired by the blue tit's highly sensitive behavior to subtle environmental changes and its reliance on short-term feedback rather than long-term path fixation during foraging, this algorithm constructs a non-cooperative perceptual search framework with environmental confidence as the core state variable. Unlike traditional swarm intelligence methods that rely on global optima or leader-led approaches, the Blue Tit Optimization Algorithm guides individuals to conduct refined searches in a trustworthy environment by jointly modeling the consistency of feedback directions during local perturbations and environmental stability. It also proactively triggers self-doubt and direction reconstruction mechanisms when the environment is uncertain, thereby achieving dynamic control of the search path. Theoretically, this algorithm can effectively suppress premature convergence and improve robustness to noise and complex objective functions, providing a new modeling approach for swarm intelligence optimization methods</span>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18367225 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Blue Tit Optimization Algorithm Zhang, Jincheng <p><span>To address the problems of premature convergence, fixed search paths, and over-reliance on local optima in traditional swarm intelligence optimization algorithms for complex, multimodal, and noisy optimization problems, this paper proposes a novel swarm intelligence optimization method—the Blue Tit Optimization Algorithm (BTOA). Inspired by the blue tit's highly sensitive behavior to subtle environmental changes and its reliance on short-term feedback rather than long-term path fixation during foraging, this algorithm constructs a non-cooperative perceptual search framework with environmental confidence as the core state variable. Unlike traditional swarm intelligence methods that rely on global optima or leader-led approaches, the Blue Tit Optimization Algorithm guides individuals to conduct refined searches in a trustworthy environment by jointly modeling the consistency of feedback directions during local perturbations and environmental stability. It also proactively triggers self-doubt and direction reconstruction mechanisms when the environment is uncertain, thereby achieving dynamic control of the search path. Theoretically, this algorithm can effectively suppress premature convergence and improve robustness to noise and complex objective functions, providing a new modeling approach for swarm intelligence optimization methods</span>.</p> |
| title | Blue Tit Optimization Algorithm |
| url | https://doi.org/10.5281/zenodo.18367225 |