@ARTICLE{10.21494/ISTE.OP.2022.0781, 

TITLE={Minimizing the cost of staff assignment using HHO and ACO algorithms}, 
  
AUTHOR={El Attaoui Anas, Norelislam El Hami, }, 
	
JOURNAL={Uncertainties and Reliability of Multiphysical Systems}, 
	
VOLUME={5}, 

NUMBER={Issue 1}, 
	
YEAR={2022}, 

URL={https://www.openscience.fr/Minimizing-the-cost-of-staff-assignment-using-HHO-and-ACO-algorithms}, 
	
DOI={10.21494/ISTE.OP.2022.0781}, 
	
ISSN={2514-569X}, 
	
ABSTRACT={In this research, two population-based, nature-inspired optimization paradigms were described, dubbed ‘’Harris Hawks Optimization’’ (HHO) and ‘’Ant Colony Optimization’’ (ACO). The real inspiration of HHO is the cooperative behavior and pursuing technique of Harris’ hawks in nature, termed "surprise pounce." At the same time, ACO is inspired by observing the behavior of actual ants. Those two natural movements were mathematically modeled to create optimization algorithms. The effectiveness of HHO and ACO optimizers is checked throughout comparisons that show that the HHO algorithm provides better results when the comparison is based on test functions, while for the case study treated, which is the planning of schedules and the minimization of the cost of staff assignment, ACO is better.}}