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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
Resumen
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.
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.
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