Saved in:
| Main Author: | |
|---|---|
| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2026
|
| Online Access: | https://doi.org/10.5281/zenodo.18366552 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- <p><span>Swarm optimization algorithms typically rely on assumptions of group cooperation and global optimum guidance, achieving gradual convergence of the search space through average information sharing among individuals. However, these methods are prone to premature convergence in complex multimodal optimization problems and path dependence on initially dominant individuals. To overcome these limitations, this paper proposes a novel swarm optimization model—the Great Black-backed Gull Optimization Algorithm (GBGO). Inspired by the opportunistic predatory behavior and highly adaptive migration mechanisms of the Great Black-backed Gull, this algorithm introduces reward deprivation-driven search, environmental reward depletion perception, failure trajectory anti-learning, and a brief hegemonic evolution mechanism to construct a non-average, non-cooperative, and competition-driven search framework. GBGO does not rely on a fixed global optimum for guidance but instead generates asymmetric search dynamics through the difference in rewards among individuals, structurally distinguishing it from existing swarm optimization algorithms. This paper systematically presents the mathematical modeling process and algorithmic mechanism analysis of GBGO, providing a new theoretical path for constructing a new generation of de-averaging swarm optimization models</span>.</p>