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Ant Colony Optimizations for Resource- and Timing-Constrained Operation Scheduling

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4 Author(s)
Gang Wang ; Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA ; Wenrui Gong ; Brian DeRenzi ; Ryan Kastner

Operation scheduling (OS) is a fundamental problem in mapping an application to a computational device. It takes a behavioral application specification and produces a schedule to minimize either the completion time or the computing resources required to meet a given deadline. The OS problem is NP-hard; thus, effective heuristic methods are necessary to provide qualitative solutions. We present novel OS algorithms using the ant colony optimization approach for both timing-constrained scheduling (TCS) and resource-constrained scheduling (RCS) problems. The algorithms use a unique hybrid approach by combining the MAX-MIN ant system metaheuristic with traditional scheduling heuristics. We compiled a comprehensive testing benchmark set from real-world applications in order to verify the effectiveness and efficiency of our proposed algorithms. For TCS, our algorithm achieves better results compared with force-directed scheduling on almost all the testing cases with a maximum 19.5% reduction of the number of resources. For RCS, our algorithm outperforms a number of different list-scheduling heuristics with better stability and generates better results with up to 14.7% improvement. Our algorithms outperform the simulated annealing method for both scheduling problems in terms of quality, computing time, and stability

Published in:

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems  (Volume:26 ,  Issue: 6 )