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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.
 | This article may be too technical for most readers to understand. (January 2023) |
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]
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