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Ιδρυματικό Αποθετήριο [SANDBOX]
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| URI | http://purl.tuc.gr/dl/dias/BA5F2A3C-BC1B-4EAD-9B24-A168177D4C8C | - |
| Αναγνωριστικό | https://doi.org/10.1007/978-3-642-17390-5_15 | - |
| Γλώσσα | en | - |
| Τίτλος | Bumble bees mating optimization algorithm for the vehicle routing problem | en |
| Δημιουργός | Marinakis Ioannis | en |
| Δημιουργός | Μαρινακης Ιωαννης | el |
| Δημιουργός | Marinaki Magdalini | en |
| Δημιουργός | Μαρινακη Μαγδαληνη | el |
| Εκδότης | Springer Verlag | en |
| Περίληψη | Recently, a number of swarm intelligence algorithms based on the behaviour of the bees have been presented. These algorithms are divided, mainly, in two categories according to the bees’ behaviour in the nature, the foraging behaviour and the mating behaviour. The most important approaches that simulate the foraging behaviour of the bees are the Artificial Bee Colony algorithm, the Virtual Bee algorithm, the Bee Colony Optimization algorithm, the BeeHive algorithm, the Bee Swarm Optimization algorithm and the Bees algorithm. Contrary to the fact that there are many algorithms that are based on the foraging behaviour of the bees, the main algorithm proposed based on the mating behaviour is the Honey Bees Mating Optimization algorithm. This chapter introduces a new algorithmic nature inspired approach based on Bumble Bees Mating Optimization for successfully solving the Vehicle Routing Problem. Bumble Bees Mating Optimization algorithm is a new population-based swarm intelligence algorithm that simulates the mating behaviour that a swarm of bumble bees perform. Two sets of benchmark instances are used in order to test the proposed algorithm with very satisfactory results. | en |
| Τύπος | Περίληψη Δημοσίευσης σε Συνέδριο | el |
| Τύπος | Conference Paper Abstract | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2015-10-28 | - |
| Ημερομηνία Δημοσίευσης | 2011 | - |
| Βιβλιογραφική Αναφορά | Y. Marinakis and M. Marinaki, “Bumble Bees Mating Optimization Algorithm for the Vehicle Routing Problem”, in Handbook of Swarm Intelligence- Concepts, Principles and Applications, 2011, pp. 347-369. doi: 10.1007/978-3-642-17390-5_15 | en |
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