Optimising Small-World Properties in VANETs with a Parallel Multi-Objective Coevolutionary Algorithm

Cooperative coevolutionary evolutionary algorithms differ from standard evolutionary algorithms architecture in that the population is split into subpopulations, each of them optimising only a subvector of the global solution vector. All subpopulations cooperate by broadcasting their local partial solutions such that each subpopulation can evaluate complete solutions. Cooperative coevolution has recently been used in evolutionary multi-objective optimisation, but few works have exploited its parallelisation capabilities or tackled real-world problems. This article proposes to apply for the first time a state-of-theart parallel asynchronous cooperative coevolutionary variant of the nondominated sorting genetic algorithm II (NSGA-II), named CCNSGA-II, on the injection network problem in vehicular ad hoc networks (VANETs). This multi-objective optimisation problem, consists in finding the minimal set of nodes with backend connectivity, referred to as injection points, to constitute a fully connected overlay that will optimise the small-world properties of the resulting network. Recently, the well-known NSGA-II algorithm was used to tackle this problem on realistic instances in the city-centre of Luxembourg. In this work we compare the performance of the CCNSGA-II to the original NSGA-II in terms of both quality of the obtained Pareto front approximations and execution time speedup.