UniSa - IRIS Institutional Research Information System
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishThe purpose of this paper is the application of Genetic Algorithms to solve the Signal Setting Design at a single junction. Two methods are compared: the monocriteria and the multicriteria optimisations. In the former case, three different objectives functions were considered: the capacity factor maximisation, the total delay minimisation and the total number of stops minimisation; in the latter case, two combinations of criteria were investigated: the total delay minimisation and the capacity factor maximisa-tion, the total delay minimisation and the total number of stops minimisation. Furthermore, two multicriteria genetic algorithms were compared: the Goldberg’s Pareto Ranking (GPR) and the Non Dominated Sorting Genetic Algorithms (NSGA-II). Conclusions discuss the effectiveness of multicrite-ria optimisation with respect to monocriteria optimisation, and the effec-tiveness of NSGA-II with respect to the GPR.
| Titolo: | Signal Setting Design at a Single Junction through the Application of Genetic Algorithms |
| Autori: | |
| Data di pubblicazione: | 2014 |
| Rivista: | |
| Abstract: | The purpose of this paper is the application of Genetic Algorithms to solve the Signal Setting Design at a single junction. Two methods are compared: the monocriteria and the multicriteria optimisations. In the former case, three different objectives functions were considered: the capacity factor maximisation, the total delay minimisation and the total number of stops minimisation; in the latter case, two combinations of criteria were investigated: the total delay minimisation and the capacity factor maximisa-tion, the total delay minimisation and the total number of stops minimisation. Furthermore, two multicriteria genetic algorithms were compared: the Goldberg’s Pareto Ranking (GPR) and the Non Dominated Sorting Genetic Algorithms (NSGA-II). Conclusions discuss the effectiveness of multicrite-ria optimisation with respect to monocriteria optimisation, and the effec-tiveness of NSGA-II with respect to the GPR. |
| Handle: | http://hdl.handle.net/11386/4267054 |
| Appare nelle tipologie: | 2.1.1 Articolo su libro con DOI |
File in questo prodotto:
Copyright © 2021