Propagation path optimization of modular product design based on hybrid genetic ant colony algorithm

In the context of modular product design, optimizing the propagation path for component information and dependencies is critical to enhancing product performance and innovation.Existing methods, such as traditional optimization algorithms, struggle to balance the complexities of multi-objective constraints, scalability, and dynamic interactions inherent in modular design systems.These approaches often fall short in addressing the trade-offs between efficiency and flexibility, particularly in real-time applications and cross-domain generalization.To overcome these challenges, we propose a hybrid genetic-ant colony optimization (GACO) algorithm, which synergistically integrates the global search capabilities of genetic algorithms with the efficient local exploration of ant colony optimization.The method features an adaptive heuristic mechanism for path evaluation and dynamic pheromone adjustment, ensuring robust convergence and adaptability across varying modular design scenarios.Empirical studies demonstrate that the GACO algorithm significantly improves solution quality, convergence speed, and adaptability compared to baseline models.The findings validate the potential of GACO as a transformative approach in modular product design, addressing key issues in propagation path optimization under the framework of intelligent design systems.