Análisis comparativo de la modificación del parámetro de inercia para la mejora en el desempeño del algoritmo PSO
DOI:
https://doi.org/10.46842/ipn.cien.v25n1a09Palabras clave:
PSO, inercia, optimización, inercia caóticaResumen
En este trabajo se presenta un desarrollo para mejorar el desempeño del algoritmo de optimización metaheurístico nombrado Particle Swarm Optimization (PSO). El algoritmo PSO está inspirado en el comportamiento que demuestran los grupos de individuos en la naturaleza, como ejemplo podemos mencionar las parvadas y los cardúmenes. Cada individuo o partícula, de forma análoga en un proceso matemático; es considerado como una posible solución y en ellos se contempla, como información relevante, su posición y la velocidad. La velocidad de cada partícula es modificada al multiplicarse por un parámetro nombrado factor de inercia y es este parámetro que proponemos modificar para mejorar el desempeño del algoritmo. La modificación del factor de inercia se desarrolla de dos maneras, decremento lineal y decremento caótico. Se han considerado las funciones de referencia Eggholder y Six-Hump Camelback, para determinar la mejora en el desempeño del algoritmo PSO. Los resultados presentados en este trabajo indican un mejor desempeño al aplicar el decremento de tipo caótico al factor de inercia.
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