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| Hovedforfatter: | |
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| Format: | Recurso digital |
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| Udgivet: |
Zenodo
2026
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| Online adgang: | https://doi.org/10.5281/zenodo.18367299 |
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Indholdsfortegnelse:
- <p><span>To address the common problems of significant search oscillations, short-sighted directional decisions, and susceptibility to local optima in traditional swarm intelligence optimization algorithms, this paper proposes a novel swarm intelligence optimization method—the Giant-Tailed Gecko Optimization (GTGO) algorithm. Inspired by the giant-tailed gecko's reliance on its tail for directional guidance, energy buffering, and risk avoidance in complex environments, this algorithm introduces a "tail-controlled" search paradigm, separating the decision-making power for search direction from the current position variable and constructing a dual-system optimization structure composed of the tail system and the body system. Through mechanisms such as tail intention mode modeling, successful direction learning, asynchronous tail-body evolution, tail morphological degradation, and tail regeneration, GTGO achieves a new dynamic balance between search stability, global exploration capability, and local exploitation capability. Structurally, this algorithm differs from existing position-driven swarm intelligence algorithms, providing a new modeling approach for swarm intelligence optimization</span>.</p>