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DC Field | Value | Language |
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dc.contributor.author | BOUMESBAH, Asma | - |
dc.contributor.author | CHERGUI, Mohamed El-Amine | - |
dc.date.accessioned | 2019-06-25T08:28:36Z | - |
dc.date.available | 2019-06-25T08:28:36Z | - |
dc.date.issued | 2019-03-05 | - |
dc.identifier.uri | http://dspace.univ-ouargla.dz/jspui/handle/123456789/20939 | - |
dc.description | Le 2eme Conference Internationale sur intelligence Artificielle et les Technologies Information ICAIIT 2019 | en_US |
dc.description.abstract | The Multi-Objective Minimum Spanning Tree prob- lem (MOST ) has been shown to be NP -hard even with two criteria. In this study we propose a hybrid GA-VNS algorithm that exploits the advantages of both ”Non-dominated Sorting Genetic Algorithm” (NSGA-II) and ”Variable Neighborhood Search” (VNS) metaheuristics to find as good an approximation as possible to the Pareto front of MOST problem. Experimental studies provide the efficiency of the method which produces solutions as close as possible to the Pareto optimal front. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Université Kasdi Merbah Ouargla | en_US |
dc.relation.ispartofseries | 2019; | - |
dc.subject | Minimum spanning tree | en_US |
dc.subject | Multiple objective linear optimization | en_US |
dc.subject | Combinatorial optimization | en_US |
dc.title | A GA-VNS based algorithm for the multi-objective spanning tree problem | en_US |
dc.type | Article | en_US |
Appears in Collections: | 2. Faculté des nouvelles technologies de l’information et de la communication |
Files in This Item:
File | Description | Size | Format | |
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BOUMESBAH Asma.pdf | 528,5 kB | Adobe PDF | View/Open |
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