Humanoid ant algorithm explained

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.

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).[6]

Notes and References

  1. Mladineo. Marko. Veza. Ivica. Gjeldum. Nikola. Single-Objective and Multi-Objective Optimization using the HUMANT algorithm. Croatian Operational Research Review. 2015. 6. 2. 459–473. 10.17535/crorr.2015.0035. free.
  2. Book: Talbi. El-Ghazali. Metaheuristics – From Design to Implementation. 2009. John Wiley & Sons.
  3. Eppe. Stefan. Application of the Ant Colony Optimization Metaheuristic to multi-objective optimization problems. Technical Report – ULB, Bruxelles. 2009.
  4. Iredi. Steffen. Merkle. Daniel. Middendorf. Martin. Bi-Criterion Optimization with Multi Colony Ant Algorithms. Evolutionary Multi-Criterion Optimization. 2001. 1993. 359–372. 10.1007/3-540-44719-9_25. Lecture Notes in Computer Science. 978-3-540-41745-3.
  5. Eppe. Stefan. Integrating the decision maker's preferences into Multi Objective Ant Colony Optimization. Proceedings of the 2nd Doctoral Symposium on. 2009.
  6. Mladineo. Marko. Veza. Ivica. Gjeldum. Nikola. Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm. International Journal of Production Research. 55. 9. 2017. 2506–2521. 10.1080/00207543.2016.1234084.

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