Self-Adaptive Ant Colony Optimisation Applied to Function Allocation in Vehicle Networks

Modern vehicles possess an increasing number of software and hardware components that are integrated in electronic control units (ECUs). Finding an optimal allocation for all components is a multi-objective optimisation problem, since every valid allocation can be rated according to multiple objectives like costs, busload, weight, etc. Additionally, several constraints mainly regarding the availability of resources have to be considered. This paper introduces a new extension to the well-known ant colony optimisation, which has been applied to the real-world problem described above. Since it concerns a multi-objective optimisation problem, multiple ant colonies are employed. In the course of this work, pheromone-updating strategies specialised on constraint-handling are developed. To reduce the effort needed to adapt the algorithm to the optimisation problem by tuning strategic parameters, self-adaptive mechanisms are established for most of them. Additionally, this step also improves the algorithm’s convergence behaviour.