Ordónez-De León, B., Aceves-Fernandez, M.A., Fernandez-Fraga, S.M., Ramos-Arreguín, J.M. and Gorrostieta-Hurtado, E. (2019) An Improved Particle Swarm Optimization (PSO): Method to Enhance Modeling of Airborne Particulate Matter (PM10). Evolving Systems, 1-10.
https://doi.org/10.1007/s12530-019-09263-y

has been cited by the following article:

• TITLE: Parameters Influencing the Optimization Process in Airborne Particles PM10 Using a Neuro-Fuzzy Algorithm Optimized with Bacteria Foraging (BFOA)

  AUTHORS: Maria del Carmen Cabrera-Hernandez, Marco Antonio Aceves-Fernandez, Juan Manuel Ramos-Arreguin, Jose Emilio Vargas-Soto, Efren Gorrostieta-Hurtado

  KEYWORDS: Air Pollution, Bacterial Foraging Optimization Algorithm (BFOA), Swarm Intelligence, ANFIS

  JOURNAL NAME: International Journal of Intelligence Science, Vol.9 No.3, July 31, 2019

  ABSTRACT: The airborne pollutants monitoring is an overriding task for humanity given that poor quality of air is a matter of public health, causing issues mainly in the respiratory and cardiovascular systems, specifically the PM10 particle. In this contribution is generated a base model with an Adaptive Neuro Fuzzy Inference System (ANFIS) which is later optimized, using a swarm intelligence technique, named Bacteria Foraging Optimization Algorithm (BFOA). Several experiments were carried with BFOA parameters, tuning them to achieve the best configuration of said parameters that produce an optimized model, demonstrating that way, how the optimization process is influenced by choice of the parameters.