An efficient quantum-behaved particle swarm optimization for multiprocessor scheduling

Abstract

Quantum-behaved particle swarm optimization (QPSO) is employed to deal with multiprocessor scheduling problem (MSP), which speeds the convergence and has few parameters to control. We combine the QPSO search technique with list scheduling to improve the solution quality in short time. At the same time, we produce the solution based on the problem-space heuristic. Several benchmark instances are tested and the experiment results demonstrate much advantage of QPSO to some other heuristics in search ability and performance. © Springer-Verlag Berlin Heidelberg 2007.