Kriging Surrogate Model-Based Constraint Multiobjective Particle Swarm Optimization Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
The main challenge when solving constrained multiobjective optimization problems (CMOPs) with intricate constraints and high dimensionality is how to overcome a problem of irregular and variable-shaped objective search regions. Such regions can lead to problems of local optimization and uneven distribution of feasible solutions. To overcome these challenges, an efficacious search method is usually needed to improve the efficiency of searching optimal solution and utilization of data structure used to store nondominated vectors. The originality of this work comes with a creative and novel design of Kriging surrogate model-based simplex crossover operator (KSCO) and Kriging surrogate model-based local search of simplex crossover operator (KLSSCO). KSCO is used to calculate the speed update equation, as well as the coefficients of the equation. KLSSCO is employed to decide which particle is treated as third particle participating in the speed update equation. A constrained multiobjective particle swarm optimization (PSO) based on KSCO and KLSSCO is proposed to solve the CMOP with local optimization and uneven distribution problems, namely KSCO and KLSSCO-based constrained multiobjective PSO algorithm (KCMOPSO). This ensures that the algorithm can search the infeasible and feasible regions of constrained multiobjective problems accurately and accelerate the convergence of the algorithm. The experimental results show that the proposed algorithm is more effective compared with the existing elite method.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it