Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/893709F9-6F73-4C2A-9F62-D29509953D6F | - |
| Αναγνωριστικό | https://doi.org/10.1007/978-3-319-09584-4_24 | - |
| Γλώσσα | en | - |
| Τίτλος | A hybrid clonal selection algorithm for the vehicle routing problem with stochastic demands | en |
| Δημιουργός | Marinaki Magdalini | en |
| Δημιουργός | Μαρινακη Μαγδαληνη | el |
| Δημιουργός | Marinakis Ioannis | en |
| Δημιουργός | Μαρινακης Ιωαννης | el |
| Δημιουργός | Athanasios Migdalas | en |
| Εκδότης | Springer Verlag | en |
| Περίληψη | The Clonal Selection Algorithm is the most known algorithm inspired from the Artificial Immune Systems and used effectively in optimization problems. In this paper, this nature inspired algorithm is used in a hybrid scheme with other metaheuristic algorithms for successfully solving the Vehicle Routing Problem with Stochastic Demands (VRPSD). More precisely, for the solution of this problem, the Hybrid Clonal Selection Algorithm (HCSA) is proposed which combines a Clonal Selection Algorithm (CSA), a Variable Neighborhood Search (VNS), and an Iterated Local Search (ILS) algorithm. The effectiveness of the original Clonal Selection Algorithm for this NP-hard problem is improved by using ILS as a hypermutation operator and VNS as a receptor editing operator. The algorithm is tested on a set of 40 benchmark instances from the literature and ten new best solutions are found. Comparisons of the proposed algorithm with several algorithms from the literature (two versions of the Particle Swarm Optimization algorithm, a Differential Evolution algorithm and a Genetic Algorithm) are also reported. | en |
| Τύπος | Αφίσα σε Συνέδριο | el |
| Τύπος | Conference Poster | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2015-10-28 | - |
| Ημερομηνία Δημοσίευσης | 2012 | - |
| Βιβλιογραφική Αναφορά | Y. Marinakis, M. Marinaki and A. Migdalas, “A Hybrid Clonal Selection Algorithm for the Vehicle Routing Problem with Stochastic Demands”, in 8th International Conference on Learning and Intelligent Optimization, 2014, pp 258-273. doi: 10.1007/978-3-319-09584-4_24 | en |
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