Propagation path optimization of modular product design based on hybrid genetic ant colony algorithm
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".