Humanoid ant algorithm: Difference between revisions

Content deleted Content added

Inline

Revision as of 02:35, 10 July 2024

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.

[1]

[2]

[3]

[4]

[5]

[6]

[7]

@@ Line 1: / Line 1: @@
 {{Technical|date=January 2023}}
-The '''Humanoid Ant algorithm''' ('''HUMANT''') <ref>{{cite journal|last1=Mladineo|first1=Marko|last2=Veza|first2=Ivica|last3=Gjeldum|first3=Nikola|title=Single-Objective and Multi-Objective Optimization using the HUMANT algorithm|journal= Croatian Operational Research Review|date=2015|volume=6|issue=2|pages=459–473|doi=10.17535/crorr.2015.0035|doi-access=free}}</ref> 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.<ref>{{cite book|last1=Talbi|first1=El-Ghazali|title=Metaheuristics – From Design to Implementation|date=2009|publisher=John Wiley & Sons}}</ref> Using decision-makers preferences, it actually turns multi-objective problem into single-objective. It is a process called scalarization of a multi-objective problem.<ref>{{cite journal|last1=Eppe|first1=Stefan|title=Application of the Ant Colony Optimization Metaheuristic to multi-objective optimization problems|journal=Technical Report – ULB, Bruxelles|date=2009}}</ref> The first Multi-Objective Ant Colony Optimization (MOACO) algorithm was published in 2001,<ref>{{cite journal|last1=Iredi|first1=Steffen|last2=Merkle|first2=Daniel|last3=Middendorf|first3=Martin|title=Bi-Criterion Optimization with Multi Colony Ant Algorithms|journal=Evolutionary Multi-Criterion Optimization|date=2001|volume=1993|pages=359–372|doi=10.1007/3-540-44719-9_25|series=Lecture Notes in Computer Science|isbn=978-3-540-41745-3}}</ref> but it was based on ''a posteriori'' approach to MOO.
+The '''humanoid ant algorithm''' ('''HUMANT''') <ref>{{cite journal|last1=Mladineo|first1=Marko|last2=Veza|first2=Ivica|last3=Gjeldum|first3=Nikola|title=Single-Objective and Multi-Objective Optimization using the HUMANT algorithm|journal= Croatian Operational Research Review|date=2015|volume=6|issue=2|pages=459–473|doi=10.17535/crorr.2015.0035|doi-access=free}}</ref> 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.<ref>{{cite book|last1=Talbi|first1=El-Ghazali|title=Metaheuristics – From Design to Implementation|date=2009|publisher=John Wiley & Sons}}</ref> Using decision-makers preferences, it actually turns multi-objective problem into single-objective. It is a process called scalarization of a multi-objective problem.<ref>{{cite journal|last1=Eppe|first1=Stefan|title=Application of the Ant Colony Optimization Metaheuristic to multi-objective optimization problems|journal=Technical Report – ULB, Bruxelles|date=2009}}</ref> The first multi-objective ant colony optimization (MOACO) algorithm was published in 2001,<ref>{{cite journal|last1=Iredi|first1=Steffen|last2=Merkle|first2=Daniel|last3=Middendorf|first3=Martin|title=Bi-Criterion Optimization with Multi Colony Ant Algorithms|journal=Evolutionary Multi-Criterion Optimization|date=2001|volume=1993|pages=359–372|doi=10.1007/3-540-44719-9_25|series=Lecture Notes in Computer Science|isbn=978-3-540-41745-3}}</ref> 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.<ref>{{cite journal|last1=Eppe|first1=Stefan|title=Integrating the decision maker's preferences into Multi Objective Ant Colony Optimization|journal=Proceedings of the 2nd Doctoral Symposium on|date=2009}}</ref>