Applying Ant Colony Optimization to the partitioned scheduling problem for heterogeneous multiprocessors

The problem of determining whether a set of periodic tasks can be assigned to a set of heterogeneous processors in such a way that all timing constraints are met has been shown, in general, to be NP-hard. This paper presents a new algorithm based on Ant Colony Optimization (ACO) metaheuristic for solving this problem. Experimental results show that our ACO approach can outperform the major existing methods. In addition to being able to search for a feasible assignment solution, our ACO approach can further optimize the solution to reduce its energy consumption.