The Humanoid Ant algorithm (HUMANT) is an ant colony optimization algorithm. The algorithm is based on ''a priori'' approach to

multi-objective optimization Multi-objective optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization) is an area of multiple criteria decision making that is concerned with ...

(MOO), 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 the

preference ranking organization method for enrichment evaluation #REDIRECT Preference ranking organization method for enrichment evaluation {{R from other capitalisation ...

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. The HUMANT algorithm has been experimentally tested on the

traveling salesman problem The travelling salesman problem (also called the travelling salesperson problem or TSP) asks the following question: "Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each cit ...

and applied to the partner selection problem with up to four objectives (criteria).

References

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