Improvement of Particle Swarm Algorithm for Multi-objective Planting Scheme Optimization and Empirical Analysis
Bibliographic record
Abstract
To address the issues of low resource utilization efficiency and poor multi-objective coordination in traditional planting scheme optimization, by integrating the particle swarm algorithm with machine learning technology, an improved particle swarm algorithm that incorporates adaptive inertia weight, chaotic disturbance mechanism, and Pareto elite retention strategy is proposed. A multi-objective planting scheme optimization model is constructed. Taking a typical agricultural area as the empirical object, multi-dimensional data such as soil, climate, and market are collected. The decision variables such as crop types, planting area, and irrigation strategy are optimized through the improved algorithm. The experimental results show that the convergence speed of the improved algorithm is 32.6% and 21.8% higher than that of the standard PSO and MOPSO algorithms respectively. The optimal planting scheme generated can increase the total crop yield of the region by 15.3%, improve the water resource utilization rate by 28.5%, and increase the economic benefits by 19.7%. This research verifies the effectiveness and superiority of the improved particle swarm algorithm in multi-objective planting optimization, providing a scientific basis and technical support for agricultural modernization planting decisions.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| 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".