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Amoebozoa represent a diverse group of terrestrial and aquatic protists that utilize lobe-shaped pseudopodia for locomotion and feeding. This characteristic differentiates them from the Rhizaria, which possess threadlike pseudopodia. The primary classifications within Amoebozoa include gymnamoebas, entamoebas, and the plasmodial and cellular slime molds. Phylogenetic evidence indicates that Amoebozoa diverged from a lineage that ultimately gave rise to fungi and animals.Gymnamoebas and...
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Un algoritmo de moldeado mejorado basado en la gestión mejor-peor para problemas de optimización numérica

Tongzheng Li1, Hongchi Meng2, Dong Wang3

  • 1Salford Business School, University of Salford, Manchester M5 4WT, UK.

Biomimetics (Basel, Switzerland)
|August 27, 2025
PubMed
Resumen
Este resumen es generado por máquina.

El novedoso algoritmo BWSMA mejora la inteligencia de enjambre mediante la integración de mecanismos adaptativos codiciosos, de gestión mejor-peor y de reemplazo estancado. Esta variante mejorada del algoritmo Slime Mould (SMA) demuestra un rendimiento superior y una robustez en las tareas de optimización.

Palabras clave:
Mecanismo codiciosoAlgoritmos metaheurísticosAlgoritmo para el molde de limoMecanismo de reemplazo estancadoInteligencia de enjambre

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Área de la Ciencia:

  • Inteligencia de enjambre
  • Inteligencia computacional
  • Algoritmos de optimización

Sus antecedentes:

  • El algoritmo Slime Mould (SMA) es un método popular de inteligencia de enjambre.
  • Las versiones existentes de SMA se enfrentan a limitaciones, incluida la convergencia lenta y el óptimo local.
  • El teorema de "no hay almuerzo gratis" destaca la necesidad de especialización y mejora de algoritmos.

Objetivo del estudio:

  • Proponer una nueva variante del algoritmo Slime Mould (SMA), llamado BWSMA.
  • Mejorar la velocidad de convergencia de la SMA, la calidad de la población y la capacidad de escapar del óptimo local.
  • Validar la eficacia y la solidez de la BWSMA a través de experimentos exhaustivos.

Principales métodos:

  • Integración de tres nuevos mecanismos en la AME: codicia adaptativa, mejor-peor gestión y reemplazo estancado.
  • Validación experimental extensa utilizando las suites de pruebas de referencia CEC2018 y CEC2022.
  • Análisis comparativo contra tres algoritmos derivados, ocho variantes de SMA y otros ocho algoritmos mejorados.
  • Análisis estadístico utilizando las pruebas de Wilcoxon, Friedman y Nemenyi.
  • Aplicación a dos problemas de optimización estructural para evaluar la aplicabilidad en el mundo real.

Principales resultados:

  • El BWSMA superó significativamente a todos los algoritmos comparados en varios conjuntos de pruebas.
  • BWSMA logró clasificaciones promedio superiores en comparación con las variantes de SMA y otros algoritmos mejorados.
  • Las pruebas estadísticas confirmaron la significativa ventaja de rendimiento del BWSMA.
  • El algoritmo demostró una gran aplicabilidad en la resolución de problemas de optimización estructural.

Conclusiones:

  • El BWSMA propuesto es un algoritmo de optimización altamente eficaz y robusto.
  • Los mecanismos integrados abordan con éxito las deficiencias de la AME original.
  • BWSMA ofrece una excelente precisión de búsqueda y es un avance prometedor en la inteligencia de enjambres.