Heterogeneous multiprocessor scheduling and allocation using evolutionary algorithms

We propose a novel stochastic approach for the problem of multiprocessor scheduling and allocation under timing and resource constraints using an evolutionary algorithm (EA). For composite schemes of DSP algorithms a compact problem encoding has been developed with emphasis on the allocation/binding part of the problem as well as an efficient problem transformation-decoding scheme in order to avoid infeasible solutions and therefore time consuming repair mechanisms. Thus, the algorithm is able to handle even large size problems within moderate computation time. Simulation results comparing the proposed EA with optimal results provided by mixed integer linear programming (MILP) show, that the EA is suitable to achieve the same or similar results but in much less time as problem size increases