@inbook{6263497d9ac846348df9aebcbb29c9a5,
title = "An evolutionary discrete firefly algorithm with novel operators for solving the vehicle routing problem with time windows",
abstract = "An evolutionary discrete version of the Firefly Algorithm (EDFA) is presented in this chapter for solving thewell-knownVehicle Routing Problem with Time Windows (VRPTW). The contribution of this work is not only the adaptation of the EDFA to the VRPTW, but also with some novel route optimization operators. These operators incorporate the process of minimizing the number of routes for a solution in the search process where node selective extractions and subsequent reinsertion are performed. The new operators analyze all routes of the current solution and thus increase the diversification capacity of the search process (in contrast with the traditional node and arc exchange based operators). With the aim of proving that the proposed EDFA and operators are effective, some different versions of the EDFA are compared. The present work includes the experimentation with all the 56 instances of the well-known VRPTW set. In order to obtain rigorous and fair conclusions, two different statistical tests have been conducted.",
keywords = "Combinatorial optimization, Discrete firefly algorithm, Firefly algorithm, Traveling salesman problem, Vehicle routing problem with time windows",
author = "Eneko Osaba and Roberto Carballedo and Yang, \{Xin She\} and Fernando Diaz",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.",
year = "2016",
doi = "10.1007/978-3-319-30235-5\_2",
language = "English",
series = "Studies in Computational Intelligence",
publisher = "Springer Verlag",
pages = "21--41",
booktitle = "Studies in Computational Intelligence",
address = "Germany",
}