| _version_ | 1866901522372100096 |
|---|---|
| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>To address the problem that traditional swarm intelligence optimization algorithms often rely on random perturbations, parameter decay, or fixed heuristics in complex multimodal problems, leading to a lack of interpretability and adaptability in search behavior, this paper proposes a novel swarm intelligence optimization method—the Frilled Lizard Optimization Algorithm (FLOA). This algorithm is based on the biological inspiration of iguanas' behavior of spreading their neck scales for deterrence and situational adjustment in uncertain environments. It introduces mechanisms such as uncertainty perception, morphological situation evolution, and morphological memory feedback to structurally model the search process. Unlike traditional algorithms that treat search jumps as random behavior, FLOA views changes in search scale as a structured decision-making process driven by cognitive states. The algorithm characterizes the current search stability of an individual through multi-source uncertainty vectors, describes the gradual change process of an individual's search pattern using continuous morphological situation variables, and achieves long-term behavioral bias through morphological memory mechanisms, thereby achieving an adaptive balance between exploration and exploitation. This algorithm provides a new approach for constructing cognitively driven swarm intelligence optimization models</span>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18367247 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Frilled Lizard Optimization Algorithm Zhang, Jincheng <p><span>To address the problem that traditional swarm intelligence optimization algorithms often rely on random perturbations, parameter decay, or fixed heuristics in complex multimodal problems, leading to a lack of interpretability and adaptability in search behavior, this paper proposes a novel swarm intelligence optimization method—the Frilled Lizard Optimization Algorithm (FLOA). This algorithm is based on the biological inspiration of iguanas' behavior of spreading their neck scales for deterrence and situational adjustment in uncertain environments. It introduces mechanisms such as uncertainty perception, morphological situation evolution, and morphological memory feedback to structurally model the search process. Unlike traditional algorithms that treat search jumps as random behavior, FLOA views changes in search scale as a structured decision-making process driven by cognitive states. The algorithm characterizes the current search stability of an individual through multi-source uncertainty vectors, describes the gradual change process of an individual's search pattern using continuous morphological situation variables, and achieves long-term behavioral bias through morphological memory mechanisms, thereby achieving an adaptive balance between exploration and exploitation. This algorithm provides a new approach for constructing cognitively driven swarm intelligence optimization models</span>.</p> |
| title | Frilled Lizard Optimization Algorithm |
| url | https://doi.org/10.5281/zenodo.18367247 |