Design space exploration using time and resource duality with the ant colony optimization
Proceedings of the 43rd annual Design Automation Conference
ACM Transactions on Design Automation of Electronic Systems (TODAES)
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems - Special issue on the 2009 ACM/IEEE international symposium on networks-on-chip
SamACO: variable sampling ant colony optimization algorithm for continuous optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An adaptive knowledge evolution strategy for finding near-optimal solutions of specific problems
Expert Systems with Applications: An International Journal
Run-time generation of partial FPGA configurations
Journal of Systems Architecture: the EUROMICRO Journal
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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