Supervisor:
Southwest Ordnance Industry Bureau
Organizer:
Chongqing Ordnance Industry Society
Chongqing University of Technology
Chongqing University of Technology
Parallel test task scheduling based on improved adaptive genetic algorithm
| DOI: | 10.11809/bqzbgcxb2023.09.040 |
| Keywords: | automatic test; parallel test; task scheduling; genetic algorithm; adaptive |
| Abstract: | A task scheduling algorithm based on improved genetic algorithm is proposed to solve the problem that parallel test task scheduling needs to avoid resource competition, system deadlock and starvation, which makes it difficult to optimize the scheduling scheme. The algorithm adopts the population dissimilarity function as the standard to evaluate the population diversity, and adaptively adjusts the crossover and mutation probability according to the population dissimilarity to ensure the population diversity in the whole iteration process. The test results and algorithm comparison in an automatic test system show that the algorithm can effectively solve the parallel test task scheduling problem, reduce the possibility of falling into the local optimal solution, improve the efficiency and accuracy of the algorithm to search the optimal solution, and achieve better search performance. |
| Issue: | Vol. 44 No. 9 (2023) |
| Published: | 2023-09-28 |
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