The humanoid ant algorithm (HUMANT) ^[1] is an ant colony optimization algorithm. The algorithm is based on a priori approach to multi-objective optimization (MOO), which means that it integrates decision-makers preferences into optimization process.^[2] Using decision-makers preferences, it actually turns multi-objective problem into single-objective. It is a process called scalarization of a multi-objective problem.^[3] The first multi-objective ant colony optimization (MOACO) algorithm was published in 2001,^[4] but it was based on a posteriori approach to MOO.

The idea of using the preference ranking organization method for enrichment evaluation to integrate decision-makers preferences into MOACO algorithm was born in 2009.^[5] HUMANT is the only known fully operational optimization algorithm that successfully integrates PROMETHEE method into ACO.^[6]

The HUMANT algorithm has been experimentally tested on the traveling salesman problem and applied to the partner selection problem with up to four objectives (criteria).^[7]

v t e Optimization: Algorithms, methods, and heuristics
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