Design and optimization of human resource scheduling strategies using intelligent evolutionary algorithms

To address the limitations of traditional algorithms in human resource scheduling optimization under multiple constraints—such as slow convergence and low constraint satisfaction rates—this study proposes a hybrid intelligent algorithm (ADE-ACO) integrating adaptive differential evolution (ADE) and ant colony optimization (ACO). First, a multi-objective optimization model for human resource scheduling is constructed. Then, an improved adaptive differential evolution algorithm is designed, which dynamically adjusts the scaling factor and crossover probability to effectively mitigate the issues of local optima stagnation and premature convergence in conventional methods. Furthermore, by incorporating an adaptive pheromone update mechanism and a multi-attribute dynamic candidate list strategy, the algorithm’s global search capability is significantly enhanced. Experimental validation on the Project Scheduling Problem Library (PSPLIB) benchmark dataset demonstrates that compared to traditional baseline algorithms including standard differential evolution (SDE) and ACO, the proposed ADE-ACO algorithm achieves a 32% significant reduction in makespan ( p < 0.01), improves resource utilization to 92.3%, while maintaining over 95% constraint satisfaction rate, conclusively proving its superiority in both convergence performance and scheduling quality.