An ACO-based approach for scheduling task graphs with communication costs

In this paper we introduce a new algorithm for computing near optimal schedules for task graph scheduling problems. In contrast to conventional approaches for solving those scheduling problems, our algorithm is based on the same principles that ants use to find shortest paths between their nest and food sources. Like their natural counterparts, artificial ants cooperate by means of pheromone trails where information about the quality of the possible solution's building blocks is stored. Based on this common communication structure, new solutions emerge by means of cooperative interaction between the ants. In the paper we demonstrate how this basic principle can be adapted to solve scheduling problems. We also evaluated the performance of the proposed ANTLS-algorithm (ant list scheduler) by means of a comprehensive test bench with more than 30,000 test cases. Compared to two conventional and two other nature-inspired approaches it performed very well.