Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
MALLBA: a software library to design efficient optimisation algorithms
International Journal of Innovative Computing and Applications
Using particle swam optimization for QoS in ad-hoc multicast
Engineering Applications of Artificial Intelligence
Ant-based topology convergence algorithms for resource management in VANETs
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Genetic algorithms for delays evaluation in networked automation systems
Engineering Applications of Artificial Intelligence
Optimizing OLSR in VANETS with differential evolution: a comprehensive study
Proceedings of the first ACM international symposium on Design and analysis of intelligent vehicular networks and applications
Swarm intelligence for traffic light scheduling: Application to real urban areas
Engineering Applications of Artificial Intelligence
Green OLSR in VANETs with differential evolution
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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The emerging field of vehicular ad hoc networks (VANETs) deals with a set of communicating vehicles which are able to spontaneously interconnect without any pre-existing infrastructure. In such kind of networks, it is crucial to make an optimal configuration of the communication protocols previously to the final network deployment. This way, a human designer can obtain an optimal QoS of the network beforehand. The problem we consider in this work lies in configuring the File Transfer protocol Configuration (FTC) with the aim of optimizing the transmission time, the number of lost packets, and the amount of data transferred in realistic VANET scenarios. We face the FTC with five representative state-of-the-art optimization techniques and compare their performance. These algorithms are: Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA), Evolutionary Strategy (ES), and Simulated Annealing (SA). For our tests, two typical environment instances of VANETs for Urban and Highway scenarios have been defined. The experiments using ns- 2 (a well-known realistic VANET simulator) reveal that PSO outperforms all the compared algorithms for both studied VANET instances.