Abstract:

To address the issues of unreasonable project activity scheduling and high expenditure， this paper proposes an improved differential evolution algorithm based on directional crossover mechanism （DirDE）. The mechanism enhances the algorithm's convergence speed by guiding the global exploration and local exploitation directions of the population. Meanwhile， this mechanism helps the algorithm jump out of the local search and avoid falling into the local optimum based on the gene guidance of the parent individuals. In the experimental section， benchmark function experiments are designed to verify the optimization ability of the DirDE. The experimental analysis results demonstrate that DirDE exhibits better convergence， accuracy， and the ability to avoid falling into local optima. Finally， the method proposed in this paper was tested through simulation experiments on a real-world linear programming model for multi-project party affairs activity scheduling optimization. The algorithm demonstrated its competitiveness and can serve as an effective tool for solving real-world party affairs activity scheduling problems.

Key words: artificial intelligence technology, evolutionary algorithm, differential evolution algorithm, party affairs activity scheduling, directional crossover mechanism