Siqi Liu,∗ Xianjun Shi,∗ Lingsong Di,∗ Liang Qin,∗ and Defeng Sun∗
Parallel test, task scheduling, genetic algorithm (GA), grey wolfoptimiser, multi-objective optimisation
Aviation equipment plays an increasingly vital role in modern high-tech warfare, with the performance of its test system directly impacting equipment reliability and combat effectiveness. However, current aviation equipment test systems encounter issues, such as resource redundancy, uneven resource utilisation, low test efficiency, and excessive test duration. To address these challenges, this study develops a parallel test task scheduling model for aviation equipment test systems and proposes an optimisation approach based on a multi-objective improved adaptive genetic hybrid gray wolf algorithm (IAGA-HGWO). This approach targets two objectives—total test time and resource balance degree—and employs the improved adaptive genetic hybrid gray wolf algorithm for optimisation. Simulation results reveal that, compared to traditional serial and semi-serial test methods, the proposed algorithm significantly reduces test time and enhances resource utilisation. Moreover, compared to single-objective optimisation methods, it mitigates the risk of converging to local optima while improving the accuracy and efficiency of optimal solution searches. This study offers a cost-effective solution for resource optimisation and efficiency improvement in aviation equipment test systems, demonstrating substantial practical value, and promising application prospects.
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