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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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The centroid of a body is a crucial concept in engineering and physics. Finding the centroid of a body can help determine its stability, its balance point, and even its design. In this context, consider a thin wire bent in the form of a quarter circular arc. Polar coordinates are used to calculate the centroid. The wire is first divided into small differential elements of a length equal to the radius multiplied by the differential angle.
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Un algoritmo de enjambre de partículas multiobjetivo basado en el mantenimiento de puntos de referencia de

Siwan Chen1, Yanmin Liu2, Jie Yang3

  • 1School of Mathematics and Statistics, Guizhou University, Guiyang, 550025, China.

Scientific reports
|December 29, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta HCRMOPSO, un novedoso algoritmo de optimización de enjambres de partículas multiobjetivo. Mejora la diversidad y la adaptabilidad, superando a los métodos existentes en problemas de referencia.

Sus antecedentes:

  • La optimización tradicional de enjambres de partículas multiobjetivo (MOPSO) enfrenta desafíos como la baja diversidad de archivos y la sensibilidad a los parámetros.
Palabras clave:
Parámetros de vueloAgrupamiento jerárquicoElección óptima individualOptimización de enjambres de partículas multiobjetivo

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  • Equilibrar la exploración global y la explotación local sigue siendo difícil en los métodos MOPSO existentes.

    • Conclusiones:

    • HCRMOPSO ofrece un avance significativo en la optimización de enjambres de partículas multiobjetivo.
    • Los métodos propuestos abordan eficazmente los problemas de diversidad y mejoran las capacidades de optimización.
    • HCRMOPSO muestra una eficacia superior para tareas complejas de optimización multiobjetivo.