Humanoid ant algorithm - Revision history

Nyq: Nyq moved page Humanoid Ant algorithm to Humanoid ant algorithm: lc per MOS:TITLECAPS

2024-07-10T02:36:08Z

Nyq moved page Humanoid Ant algorithm to Humanoid ant algorithm: lc per MOS:TITLECAPS

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lc per MOS:EXPABBR and other common nouns

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	{{Technical\|date=January 2023}}		{{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>		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>

0i burabo noi 5 at 13:32, 3 November 2023

2023-11-03T13:32:09Z

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	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>		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>
	~~So far{{As of?\|date=January 2023}},~~ HUMANT ~~algorithm~~ is only known fully operational optimization algorithm that successfully ~~integrated~~ PROMETHEE method into ACO.{{Citation ~~needed~~\|date=~~January~~ 2023}}		HUMANT is the only known fully operational optimization algorithm that successfully integrates PROMETHEE method into ACO.<ref>{{Citation \|last=Al-Janabi \|first=Rana JumaaSarih \|title=Multi-key Encryption Based on RSA and Block Segmentation \|date=2022 \|url=http://dx.doi.org/10.1007/978-981-16-8739-6_61 \|work=Biologically Inspired Techniques in Many Criteria Decision Making \|pages=687–695 \|access-date=2023-11-03 \|place=Singapore \|publisher=Springer Nature Singapore \|isbn=978-981-16-8738-9 \|last2=Al-Jubouri \|first2=Ali Najam Mahawash}}</ref>

	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).<ref>{{cite journal\|last1=Mladineo\|first1=Marko\|last2=Veza\|first2=Ivica\|last3=Gjeldum\|first3=Nikola\|title=Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm\|journal=International Journal of Production Research\|volume=55\|issue=9\|date=2017\|pages=2506–2521\|doi=10.1080/00207543.2016.1234084}}</ref>		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).<ref>{{cite journal\|last1=Mladineo\|first1=Marko\|last2=Veza\|first2=Ivica\|last3=Gjeldum\|first3=Nikola\|title=Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm\|journal=International Journal of Production Research\|volume=55\|issue=9\|date=2017\|pages=2506–2521\|doi=10.1080/00207543.2016.1234084}}</ref>

Jarble: adding Template:Optimization algorithms

2023-07-01T19:35:43Z

adding Template:Optimization algorithms

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	{{Reflist}}		{{Reflist}}
	{{Improve categories\|date=January 2023}}		{{Improve categories\|date=January 2023}}
			{{Optimization algorithms}}
	[[Category:Nature-inspired metaheuristics]]		[[Category:Nature-inspired metaheuristics]]

Pppery: Cleanup, trim tech detail

2023-01-15T21:49:40Z

Cleanup, trim tech detail

← Previous revision		Revision as of 21:49, 15 January 2023
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			{{Technical\|date=January 2023}}
	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]].
		⚫	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>
⚫	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.
		⚫	So far{{As of?\|date=January 2023}}, HUMANT algorithm is only known fully operational optimization algorithm that successfully integrated PROMETHEE method into ACO.{{Citation needed\|date=January 2023}}

		⚫	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).<ref>{{cite journal\|last1=Mladineo\|first1=Marko\|last2=Veza\|first2=Ivica\|last3=Gjeldum\|first3=Nikola\|title=Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm\|journal=International Journal of Production Research\|volume=55\|issue=9\|date=2017\|pages=2506–2521\|doi=10.1080/00207543.2016.1234084}}</ref>
⚫	The idea of using [[~~Preference~~ ranking organization method for enrichment evaluation~~\|PROMETHEE method~~]] 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>
⚫	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).<ref>{{cite journal\|last1=Mladineo\|first1=Marko\|last2=Veza\|first2=Ivica\|last3=Gjeldum\|first3=Nikola\|title=Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm\|journal=International Journal of Production Research\|volume=55\|issue=9\|date=2017\|pages=2506–2521\|doi=10.1080/00207543.2016.1234084}}</ref>

	== References ==		== References ==
	{{Reflist}}		{{Reflist}}
			{{Improve categories\|date=January 2023}}

	[[Category:Nature-inspired metaheuristics]]		[[Category:Nature-inspired metaheuristics]]

RMCD bot: Removing notice of move discussion

2023-01-04T00:05:16Z

Removing notice of move discussion

← Previous revision		Revision as of 00:05, 4 January 2023
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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]].
	<noinclude>{{User:RMCD bot/subject notice\|1=Humanoid Ant algorithm\|2=Talk:AEC (Alashki Engineering Constructions)#Requested move 27 December 2022}}
⚫	~~</noinclude>~~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]].

	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.

Fathoms Below: The Night Watch moved page HUMANT (HUManoid ANT) algorithm to Humanoid Ant algorithm: Per RM

2023-01-04T00:02:59Z

The Night Watch moved page HUMANT (HUManoid ANT) algorithm to Humanoid Ant algorithm: Per RM

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← Previous revision		Revision as of 23:19, 27 December 2022
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			<noinclude>{{User:RMCD bot/subject notice\|1=Humanoid Ant algorithm\|2=Talk:AEC (Alashki Engineering Constructions)#Requested move 27 December 2022}}
	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]].		</noinclude>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]].

	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.

161.53.169.191 at 10:41, 3 May 2021

2021-05-03T10:41:10Z

← Previous revision		Revision as of 10:41, 3 May 2021
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	So far, HUMANT algorithm is only known fully operational optimization algorithm that successfully integrated PROMETHEE method into ACO.		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).<ref>{{cite journal\|last1=Mladineo\|first1=Marko\|last2=Veza\|first2=Ivica\|last3=Gjeldum\|first3=Nikola\|title=Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm\|journal=International Journal of Production Research\|volume=55\|issue=9\|date=~~2016~~\|pages=2506–2521\|doi=10.1080/00207543.2016.1234084}}</ref>		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).<ref>{{cite journal\|last1=Mladineo\|first1=Marko\|last2=Veza\|first2=Ivica\|last3=Gjeldum\|first3=Nikola\|title=Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm\|journal=International Journal of Production Research\|volume=55\|issue=9\|date=2017\|pages=2506–2521\|doi=10.1080/00207543.2016.1234084}}</ref>

	== References ==		== References ==

OAbot: Open access bot: doi added to citation with #oabot.

2020-04-17T20:32:51Z

Open access bot: doi added to citation with #oabot.

← Previous revision		Revision as of 20:32, 17 April 2020
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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}}</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]].		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]].

	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.

← Previous revision		Revision as of 02:35, 10 July 2024
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	{{Technical\|date=January 2023}}		{{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>		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>

← Previous revision		Revision as of 13:32, 3 November 2023
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	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>		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>
	~~So far{{As of?\|date=January 2023}},~~ HUMANT ~~algorithm~~ is only known fully operational optimization algorithm that successfully ~~integrated~~ PROMETHEE method into ACO.{{Citation ~~needed~~\|date=~~January~~ 2023}}		HUMANT is the only known fully operational optimization algorithm that successfully integrates PROMETHEE method into ACO.<ref>{{Citation \|last=Al-Janabi \|first=Rana JumaaSarih \|title=Multi-key Encryption Based on RSA and Block Segmentation \|date=2022 \|url=http://dx.doi.org/10.1007/978-981-16-8739-6_61 \|work=Biologically Inspired Techniques in Many Criteria Decision Making \|pages=687–695 \|access-date=2023-11-03 \|place=Singapore \|publisher=Springer Nature Singapore \|isbn=978-981-16-8738-9 \|last2=Al-Jubouri \|first2=Ali Najam Mahawash}}</ref>

	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).<ref>{{cite journal\|last1=Mladineo\|first1=Marko\|last2=Veza\|first2=Ivica\|last3=Gjeldum\|first3=Nikola\|title=Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm\|journal=International Journal of Production Research\|volume=55\|issue=9\|date=2017\|pages=2506–2521\|doi=10.1080/00207543.2016.1234084}}</ref>		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).<ref>{{cite journal\|last1=Mladineo\|first1=Marko\|last2=Veza\|first2=Ivica\|last3=Gjeldum\|first3=Nikola\|title=Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm\|journal=International Journal of Production Research\|volume=55\|issue=9\|date=2017\|pages=2506–2521\|doi=10.1080/00207543.2016.1234084}}</ref>

← Previous revision		Revision as of 23:19, 27 December 2022
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			<noinclude>{{User:RMCD bot/subject notice\|1=Humanoid Ant algorithm\|2=Talk:AEC (Alashki Engineering Constructions)#Requested move 27 December 2022}}
	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]].		</noinclude>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]].

	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.