Humanoid ant algorithm

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]

References

^ Mladineo, Marko; Veza, Ivica; Gjeldum, Nikola (2015). "Single-Objective and Multi-Objective Optimization using the HUMANT algorithm". Croatian Operational Research Review. 6 (2): 459–473. doi:10.17535/crorr.2015.0035.
^ Talbi, El-Ghazali (2009). Metaheuristics – From Design to Implementation. John Wiley & Sons.
^ Eppe, Stefan (2009). "Application of the Ant Colony Optimization Metaheuristic to multi-objective optimization problems". Technical Report – ULB, Bruxelles.
^ Iredi, Steffen; Merkle, Daniel; Middendorf, Martin (2001). "Bi-Criterion Optimization with Multi Colony Ant Algorithms". Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science. 1993: 359–372. doi:10.1007/3-540-44719-9_25. ISBN 978-3-540-41745-3.
^ Eppe, Stefan (2009). "Integrating the decision maker's preferences into Multi Objective Ant Colony Optimization". Proceedings of the 2nd Doctoral Symposium on.
^ Al-Janabi, Rana JumaaSarih; Al-Jubouri, Ali Najam Mahawash (2022), "Multi-key Encryption Based on RSA and Block Segmentation", Biologically Inspired Techniques in Many Criteria Decision Making, Singapore: Springer Nature Singapore, pp. 687–695, ISBN 978-981-16-8738-9, retrieved 2023-11-03
^ Mladineo, Marko; Veza, Ivica; Gjeldum, Nikola (2017). "Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm". International Journal of Production Research. 55 (9): 2506–2521. doi:10.1080/00207543.2016.1234084.

[1] Mladineo, Marko; Veza, Ivica; Gjeldum, Nikola (2015). "Single-Objective and Multi-Objective Optimization using the HUMANT algorithm". Croatian Operational Research Review. 6 (2): 459–473. doi:10.17535/crorr.2015.0035.

[2] Talbi, El-Ghazali (2009). Metaheuristics – From Design to Implementation. John Wiley & Sons.

[3] Eppe, Stefan (2009). "Application of the Ant Colony Optimization Metaheuristic to multi-objective optimization problems". Technical Report – ULB, Bruxelles.

[4] Iredi, Steffen; Merkle, Daniel; Middendorf, Martin (2001). "Bi-Criterion Optimization with Multi Colony Ant Algorithms". Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science. 1993: 359–372. doi:10.1007/3-540-44719-9_25. ISBN 978-3-540-41745-3.

[5] Eppe, Stefan (2009). "Integrating the decision maker's preferences into Multi Objective Ant Colony Optimization". Proceedings of the 2nd Doctoral Symposium on.

[6] Al-Janabi, Rana JumaaSarih; Al-Jubouri, Ali Najam Mahawash (2022), "Multi-key Encryption Based on RSA and Block Segmentation", Biologically Inspired Techniques in Many Criteria Decision Making, Singapore: Springer Nature Singapore, pp. 687–695, ISBN 978-981-16-8738-9, retrieved 2023-11-03

[7] Mladineo, Marko; Veza, Ivica; Gjeldum, Nikola (2017). "Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm". International Journal of Production Research. 55 (9): 2506–2521. doi:10.1080/00207543.2016.1234084.

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