Parallel Test Scheduling Based on Adaptive Differential Evolution Algorithm

To address the issues of resource contention, deadlock, and premature convergence in parallel test task scheduling for complex systems, this paper proposes an adaptive differential evolution algorithm based on population dissimilarity. The algorithm employs integer encoding to represent task scheduling sequences, designs a population initialization method based on task dependency constraints, and dynamically evaluates population diversity using Kendall's Tau correlation coefficient. It adaptively adjusts the length of mutation subsequences to balance global exploration and local exploitation capabilities. Additionally, the algorithm integrates crossover-selection operators and timedriven fitness function optimization to ensure that scheduling schemes satisfy task priorities and resource constraints. Simulation results demonstrate that, compared to traditional differential evolution algorithms, the proposed algorithm significantly improves convergence speed, scheduling efficiency, and stability, effectively avoiding premature convergence. The proposed algorithm provides an efficient and reliable optimization method for parallel test task scheduling in complex systems.