Optimization study of urban elderly manpower resource development strategy based on differential evolutionary algorithm
Bibliographic record
Abstract
In the context of urban elderly human resource development, differential evolutionary algorithms can be used to optimize the development strategy and improve the efficiency of resource utilization.The study constructs a multi-objective scheduling optimization model for human resources based on an improved differential evolutionary algorithm, which searches for the optimal development strategy by simulating the mutation, crossover, and selection operations in the process of biological evolution.In addition, the model combines a multi-objective feature selection algorithm to capture the data information of urban elderly resource development more accurately and ensure the scientific and practicality of the strategy.The pareto front of this paper's algorithm on the optimal solution test function is more in line with the real frontier, and the GD value is between 0.00171 and 0.0325, which has better convergence.The execution time of this algorithm for elderly manpower resource scheduling is shortened compared to the comparison algorithm, and the convergence of different task sizes is accomplished when iterating to 110~150 rounds.The ADE-MOFS algorithm has the lowest running cost and the shortest completion period on elderly manpower resource scheduling.The research in this paper shows new ideas and methods for the rational development and utilization of urban elderly manpower resources, which has important theoretical and temporal significance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".