TY - JOUR
T1 - On the application of bio-inspired heuristics for network routing with multiple QoS constraints
AU - Bilbao, Miren Nekane
AU - Perfecto, Cristina
AU - Del Ser, Javier
AU - Landa, Xabier
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Since the advent of Telecommunication networks in the early 60’s,routing has become a recurrent problem with evergrowing complexity due to the simultaneous share of resources,stringent Quality of Service (QoS) constraints and unmanageable network scales (size,speed and exchanged data volume) by conventional route finding schemes. This paper considers a particular class of routing problems where the route to be found needs to simultaneously fulfill different requirements in terms of e.g. maximum latency,loss rate or any other cost measure. The manuscript delves into the application of the Coral Reefs Optimization and the Firefly Algorithm,two of the latest bio-inspired meta-heuristic techniques reported to outperform other approximative solvers in a wide range of optimization scenarios. Results obtained from Monte Carlo simulations over synthetic network instances will shed light on the comparative performance of these two algorithms,with emphasis on their convergence speed and statistical significance.
AB - Since the advent of Telecommunication networks in the early 60’s,routing has become a recurrent problem with evergrowing complexity due to the simultaneous share of resources,stringent Quality of Service (QoS) constraints and unmanageable network scales (size,speed and exchanged data volume) by conventional route finding schemes. This paper considers a particular class of routing problems where the route to be found needs to simultaneously fulfill different requirements in terms of e.g. maximum latency,loss rate or any other cost measure. The manuscript delves into the application of the Coral Reefs Optimization and the Firefly Algorithm,two of the latest bio-inspired meta-heuristic techniques reported to outperform other approximative solvers in a wide range of optimization scenarios. Results obtained from Monte Carlo simulations over synthetic network instances will shed light on the comparative performance of these two algorithms,with emphasis on their convergence speed and statistical significance.
KW - Bio-inspired optimization
KW - Constrained network routing
KW - Coral reefs optimization
KW - Firefly algorithm
UR - http://www.scopus.com/inward/record.url?scp=84992411185&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-48829-5_19
DO - 10.1007/978-3-319-48829-5_19
M3 - Article
AN - SCOPUS:84992411185
SN - 1860-949X
VL - 678
SP - 195
EP - 204
JO - Studies in Computational Intelligence
JF - Studies in Computational Intelligence
ER -