Humanoid ant algorithm: Difference between revisions
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| ⚫ | HUMANT (HUManoid ANT) algorithm<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> belongs to [[Ant colony optimization algorithms]]. It is a Multi-Objective Ant Colony Optimization (MOACO) with ''a priori'' approach to [[Multi-objective optimization|Multi-Objective Optimization]] (MOO), based on Max-Min Ant System (MMAS) and [[Multi-Criteria Decision Analysis|multi-criteria decision-making]] [[Preference ranking organization method for enrichment evaluation|PROMETHEE method]]. |
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The algorithm is based on ''a priori'' approach to Multi-Objective Optimization, 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 algorithm is based on ''a priori'' approach to Multi-Objective Optimization, 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. |
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Revision as of 00:05, 4 January 2023
HUMANT (HUManoid ANT) algorithm[1] belongs to Ant colony optimization algorithms. It is a Multi-Objective Ant Colony Optimization (MOACO) with a priori approach to Multi-Objective Optimization (MOO), based on Max-Min Ant System (MMAS) and multi-criteria decision-making PROMETHEE method.
The algorithm is based on a priori approach to Multi-Objective Optimization, 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 PROMETHEE method to integrate decision-makers preferences into MOACO algorithm was born in 2009.[5] So far, HUMANT algorithm is only known fully operational optimization algorithm that successfully integrated PROMETHEE method into ACO.
HUMANT algorithm has been experimentally tested on the Traveling salesman problem and applied to the Partner selection problem (PSP) with up to four objectives (criteria).[6]
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.
- ^ 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.