Research on multi-objective optimization strategy and algorithm improvement based on genetic algorithm in large-scale computing environment
Bibliographic record
Abstract
The basic genetic algorithm suffers from problems such as precocity and low search efficiency when solving multi-objective optimization problems in large-scale computing environments.Aiming at these problems, this paper introduces various improvement strategies such as neighborhood operation, adaptive strategy, chaos optimization and cooling into the classical genetic algorithm, and designs an improved genetic algorithm process that organically combines various improvement strategies.The improved genetic algorithm and other existing large-scale multi-objective optimization algorithms are tested using LSMOP test problems, and the improved genetic algorithm has better convergence and diversity than other algorithms on both two-objective and three-objective LSMOP test problems.The PF curves of the seven algorithms are plotted separately for the two-objective on LSMOP6 and the three-objective on LSMOP5 when the decision variable is 200, and the images show that the improved genetic algorithm has the most uniform population distribution.The experimental results confirm the effectiveness of the improved genetic algorithm in solving large-scale multiobjective optimization problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".