HUMANT (HUManoid ANT) algorithm belongs to Ant colony optimization algorithms. It is a Multi-Objective Ant Colony Optimization (MOACO) with a priori approach to Multi-Objective Optimization (MOO), based on Max-Min Ant System (MMAS) and multi-criteria decision-making PROMETHEE method.
The algorithm is based on a priori approach to Multi-Objective Optimization, which means that it integrates decision-makers preferences into optimization process. Using decision-makers preferences, it actually turns multi-objective problem into single-objective. It is a process called scalarization of a multi-objective problem. The first Multi-Objective Ant Colony Optimization (MOACO) algorithm was published in 2001, but it was based on a posteriori approach to MOO.
The idea of using PROMETHEE method to integrate decision-makers preferences into MOACO algorithm was born in 2009. So far, HUMANT algorithm is only known fully operational optimization algorithm that successfully integrated PROMETHEE method into ACO.
HUMANT algorithm has been experimentally tested on the Traveling salesman problem and applied to the Partner selection problem (PSP) with up to four objectives (criteria).