Optimization Algorithm Inspired by Seawater Conductivity Variation

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Autore principale: Zhang, Jincheng
Natura: Recurso digital
Pubblicazione: Zenodo 2025
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author Zhang, Jincheng
author_facet Zhang, Jincheng
contents <p><span>Seawater electrical conductivity (SEC) is a key physical quantity that describes the electrical conductivity of seawater. Its variations are complexly influenced by temperature, salinity, and pressure. This paper proposes an optimization algorithm inspired by variations in seawater conductivity to simulate the search behavior of seawater microclusters under gradient drift, surge disturbances, local diffusion, and conductive-induced attraction mechanisms. The algorithm systematically describes the position update formula and fitness evaluation method for search particles through mathematical modeling, and proposes an innovative conductive-induced attraction mechanism that enables groups to form search clusters based on conductivity similarity, achieving efficient global convergence. This paper systematically expounds on the algorithm's design principles, mathematical formulas, and update mechanisms, providing a new heuristic approach for continuous optimization problems.</span></p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_17182985
institution Zenodo
language
publishDate 2025
publisher Zenodo
record_format zenodo
spellingShingle Optimization Algorithm Inspired by Seawater Conductivity Variation
Zhang, Jincheng
<p><span>Seawater electrical conductivity (SEC) is a key physical quantity that describes the electrical conductivity of seawater. Its variations are complexly influenced by temperature, salinity, and pressure. This paper proposes an optimization algorithm inspired by variations in seawater conductivity to simulate the search behavior of seawater microclusters under gradient drift, surge disturbances, local diffusion, and conductive-induced attraction mechanisms. The algorithm systematically describes the position update formula and fitness evaluation method for search particles through mathematical modeling, and proposes an innovative conductive-induced attraction mechanism that enables groups to form search clusters based on conductivity similarity, achieving efficient global convergence. This paper systematically expounds on the algorithm's design principles, mathematical formulas, and update mechanisms, providing a new heuristic approach for continuous optimization problems.</span></p>
title Optimization Algorithm Inspired by Seawater Conductivity Variation
url https://doi.org/10.5281/zenodo.17182985