Paper Info
Title
A Case Study Approach To Automatic Driving Train Using Cbr With Differential Evolution
Abstract
This paper presents a system applied in the automatic driving train using Case Based Reasoning (CBR) with Differential Evolution (DE). CBR was used to retrieve, reuse and revise experiences from real data during the journey. The DE was used to adapt the cases retrieved and optimize then considering multi-objective optimization.For this purpose, a train driving simulator used and the results compared the data of real train driving scenarios. Multi-objective optimization was used to reduce fuel consumption and also travel time. The results obtained concerning fuel consumption was entirely satisfactory because in some cases average savings of 45% in fuel consumption about the results obtained by human drivers. Adapting cases using the Differential Evolution approach led to a 5% gain in consumption over an adaptation of cases using Genetic Algorithm. It should also note that the time required for case adaptation was lower using Differential Evolution Algorithm than using Genetic Algorithm.
Year
DOI
Venue
2018
10.1109/SMC.2018.00614
2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
Keywords
Field
DocType
Driving Train, Differential Evolution, Case Based Reasoning
Driving simulator,Computer science,Reuse,Control engineering,Differential evolution,Artificial intelligence,Fuel efficiency,Case-based reasoning,Travel time,Differential evolution algorithm,Genetic algorithm,Machine learning
Conference
ISSN
Citations 
PageRank 
1062-922X
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Viviane Dal Molin de Souza100.34
Osmar Betazzi Dordal275.02
Denise Maria Vecino Sato383.98
Lucas Fabre400.34
Edson Emílio Scalabrin53614.52
Bráulio Coelho Ávila62210.63
André Pinz Borges7147.67