Multi-objective routing and topology optimization in networked embedded systems

Modern networked embedded system design has to cope with multiple design objectives. One major challenge is the determination of optimal routings with respect to these objectives. Existing automatic optimization approaches carry out a two step optimization: First, they perform a multi-objective topology optimization of the networked embedded system. Then, a multi-objective routing optimization for a subset of Pareto optimal solutions obtained from the first step is performed. In general, this may exclude several globally optimal solutions from the optimization process. To overcome this drawback, a unified approach based on multi-objective evolutionary algorithms is presented that ensures a combined optimization of the topology and routing. Since the system topology is varied within the optimization, the main contribution of this paper contribution is a novel routing technique that always samples feasible paths using a topology independent genetic encoding. This encoding preserves optimized routing information when changing the underlying topology. An experimental evaluation shows the effectiveness of the presented approach.