Paper Info
Title
A memetic algorithm for computing multicriteria shortest paths in stochastic multimodal networks.
Abstract
Modern transport systems are nowadays very complex. Building Advanced Travelers Information Systems (ATIS) has therefore become a certain need. Since passengers do not only seek a short-time travel, but they tend to optimize several criteria, an efficient routing system should incorporate a multi-objective analysis into its search process. Besides, the transport system may behave in an uncertain manner. Therefore, integrating uncertainty into routing algorithms may provide better itineraries. The main objective of this work is to propose a Memetic Approach (MA) in which a Genetic Algorithm (GA) is combined with a Hill Climbing (HC) local search procedure in order to solve the multicriteria shortest path problem in stochastic multimodal transport networks. Experimental results have been assessed by solving real life itinerary problems defined on the transport network of the city of Paris and its suburbs. Results indicate that unlike classical deterministic algorithms and pure GA and hill climbing, the proposed MA provide better itineraries within a reasonable amount of time.
Year
DOI
Venue
2017
10.1145/3067695.3076064
GECCO (Companion)
Keywords
Field
DocType
Multicriteria optimization, stochastic multimodal networks, genetic algorithms, hill climbing, memetic algorithms
Memetic algorithm,Hill climbing,Mathematical optimization,Shortest path problem,Computer science,Multi-objective optimization,Artificial intelligence,Local search (optimization),Multimodal transport,Genetic algorithm,Transport network,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
2
Authors
4
Name
Order
Citations
PageRank
omar dib191.97
Alexandre Caminada210723.61
Marie-Ange Manier37812.49
Laurent Moalic4307.19