Ιδρυματικό Αποθετήριο
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Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/E0DEEE61-404A-44CC-968E-8613B615F4E8 | - |
| Αναγνωριστικό | https://doi.org/10.1145/2001576.2001591 | - |
| Γλώσσα | en | - |
| Μέγεθος | 8 pages | en |
| Τίτλος | A honey bees mating optimization algorithm for the open vehicle routing problem | en |
| Δημιουργός | Marinakis Ioannis | en |
| Δημιουργός | Μαρινακης Ιωαννης | el |
| Δημιουργός | Marinaki Magdalini | en |
| Δημιουργός | Μαρινακη Μαγδαληνη | el |
| Εκδότης | Association for Computing Machinery | en |
| Περίληψη | Honey Bees Mating Optimization algorithm is a relatively new nature inspired algorithm. In this paper, this nature inspired algorithm is used in a hybrid scheme with other metaheuristic algorithms for successfully solving the Open Vehicle Routing Problem. More precisely, the proposed algorithm for the solution of the Open Vehicle Routing Problem, the Honey Bees Mating Optimization (HBMOOVRP), combines a Honey Bees Mating Optimization (HBMO) algorithm and the Expanding Neighborhood Search (ENS) algorithm. Two set of benchmark instances is used in order to test the proposed algorithm. The results obtained for both sets are very satisfactory. More specifically, in the fourteen instances proposed by Christofides, the average quality is 0.35% when a hierarchical objective function is used, where, first, the number of vehicles is minimized and, afterwards, the total travel distance is minimized and the average quality is 0.42% when only the travel distance is minimized, while for the eight instances proposed by Li et al. when a hierarchical objective function is used the average quality is 0.21%. | en |
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Full Paper | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2015-11-06 | - |
| Ημερομηνία Δημοσίευσης | 2011 | - |
| Βιβλιογραφική Αναφορά | Y. Marinakis ,M. Marinaki, " A honey bees mating optimization algorithm for the open vehicle routing problem,in 2011 Genetic and Evol. Computation Conf.,pp.101-108.doi :10.1145/2001576.2001591 | en |
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