preprint · Zenodo (CERN European Organization for Nuclear Research) · 2026

Nematomorpha Optimization Algorithm

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Abstract

omplex optimization problems by simulating the collective behavior of organisms in nature. This paper proposes a novel swarm intelligence optimization algorithm—the Nematomorpha Optimization Algorithm (NOA). The algorithm draws inspiration from the behavior of horsehair worms in nature, which involve crawling, contracting, extending, and sensing the environment to find water sources. It constructs a multi-stage crawling mechanism, an energy-information coupling mechanism, an environmentally sensitive perception mechanism, and a hierarchical group cooperation mechanism to achieve an effective balance between global search and local exploitation. In the algorithm design, mathematical formulas are introduced to accurately model the mechanisms of individual crawling, step size adjustment, energy updating, and group cooperation. The characteristics and potential application scenarios of the algorithm are analyzed. The results show that NOA has strong global search and local exploitation capabilities in high-dimensional continuous optimization problems, providing a new approach to swarm intelligence optimization algorithms.

Cite this paper

APA 7

Zhang, J. (2026). Nematomorpha Optimization Algorithm. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.18240810

MLA 9

Zhang, Jincheng. “Nematomorpha Optimization Algorithm.” Zenodo (CERN European Organization for Nuclear Research), 2026, https://doi.org/10.5281/zenodo.18240810.

Chicago

Zhang, Jincheng. 2026. “Nematomorpha Optimization Algorithm.” Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.18240810.

Harvard

Zhang, J. (2026) ‘Nematomorpha Optimization Algorithm’, Zenodo (CERN European Organization for Nuclear Research). Available at: https://doi.org/10.5281/zenodo.18240810.

BibTeX

@misc{zhang2026nematomorpha,
  title = {{Nematomorpha Optimization Algorithm}},
  author = {Zhang, Jincheng},
  journal = {Zenodo (CERN European Organization for Nuclear Research)},
  year = {2026},
  doi = {10.5281/zenodo.18240810}
}

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