שמור ב:
| מחבר ראשי: | |
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| פורמט: | Recurso digital |
| שפה: | |
| יצא לאור: |
Zenodo
2025
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| גישה מקוונת: | https://doi.org/10.5281/zenodo.17168536 |
| תגים: |
הוספת תג
אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!
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תוכן הענינים:
- <p><span>Optimization algorithms have widespread applications in fields such as engineering, economics, artificial intelligence, and computational science. Traditional optimization algorithms often struggle with local optima, slow convergence, and insufficient search space diversity when dealing with complex, high-dimensional, and multimodal functions. This paper proposes a novel heuristic optimization algorithm, the Tube Worm Optimization (TWO), inspired by the ecological behavior of deep-sea tubeworms, including chemical gradient perception, symbiotic information exchange, and tube contraction and jump mechanisms. TWO achieves an effective balance between global search and local refinement through three innovative mechanisms. The algorithm's core innovations lie in the introduction of a dynamic gradient-sensitive step size, multi-neighborhood weighted information fusion, and a directional jump mechanism. The algorithm's mathematical model is fully described in plain text. This method not only reflects the ecological characteristics of tubeworms but also provides a clear and quantifiable mathematical framework, offering new insights and tools for solving complex optimization problems.</span></p>