An Efficient Quantum-Behaved Particle Swarm Optimization for Multiprocessor Scheduling

Authors:
Xiaohong Kong;Jun Sun;Bin Ye;Wenbo Xu
Affiliations:
School of Information Technology, Southern Yangtze University, Wuxi 214122, China and Henan Institute Of Science and Technology, Xinxiang, Henan 453003, China;School of Information Technology, Southern Yangtze University, Wuxi 214122, China;School of Information Technology, Southern Yangtze University, Wuxi 214122, China;School of Information Technology, Southern Yangtze University, Wuxi 214122, China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Year:
2007

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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.