Adaptive Strategy-Enhanced NSGA-II for Multi-Objective Optimization with Improved Convergence and Diversity Control
Bibliographic record
Abstract
In modern society, sustainability has become an increasingly important issue. By solving multi-objective problems, decision-makers can make more sustainable decisions. To efficiently solve multi-objective problems, an adaptive strategy is proposed to optimize the crossover and mutation operators of the nondominated sorting genetic algorithm II (NSGA-II). Moreover, the multi-objective flexible job shop scheduling problem is modeled by incorporating worker fatigue factors. Finally, the algorithm performance was tested using ZDT and DTLZ series test functions, and the multi-objective solving performance of the algorithm was tested based on standard examples FMk01-FMk06.The results showed that in the ZDT1 and ZDT2 test functions, the solution set coverage of the proposed algorithm was 0.833 and 0.906, respectively, and the inverse generation distance was 0.006 and 0.0059, respectively, achieving better convergence and diversity. In the DTLZ1 test function, the inverse generation distance of the proposed algorithm did not exceed 2. In the FMk03 example, the inverse generation distance of the proposed algorithm was 0.009, which was lower than the traditional NSGA-II algorithm. In the FMk06 example, the proposed algorithm achieved a super volume of 0.37, which was higher than the multiobjective squirrel search algorithm and NSGA-III algorithm. The experiment has demonstrated the effectiveness of the improved algorithm in solving multi-objective issues. The research results contribute to improving the efficiency of addressing multi-objective optimization and complex problems in real life, enhancing the scientificity and effectiveness of decision-making.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".