Multiprocessor scheduling in a genetic paradigm
Parallel Computing
Efficient scheduling of arbitrary task graphs to multiprocessors using a parallel genetic algorithm
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Journal of Global Optimization
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers
Computers and Operations Research
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Employing a differential evolution (DE) algorithm, we present a novel permutation-based search technique in list scheduling for parallel program. By encoding a vector as a scheduling list and differential variation as s swap operator, the DE algorithm can generate high quality solutions in a short time. In standard differential evolution algorithm, while constructing the next generation, a greedy strategy is used which maybe lead to convergence to a local optimum. In order to avoid the above problem, we combine differential evolution algorithm with simulated annealing algorithm which relaxes the criterion selecting the next generation. We also use stochastic topological sorting algorithm (STS) to generate an initial scheduling list. The results demonstrate that the hybrid differential evolution generates better solutions even optimal solutions in most cases and simultaneously meet scalability.