Parameter tuning of the HCSCROCFO-3Opt algorithm for solving the capacitated vehicle routing problem
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes the cuckoo search (CS), central force optimization (CFO), chemical reaction optimization (CRO) and 3-Opt for solving the capacitated vehicle routing problem (CVRP). HCSCROCFO-3Opt, which is the parallel hybrid algorithm that is proposed, is a form of augmented HCSCROCFO with a local search process founded on CS that utilizes positive aspects of the other optimization approaches including CRO and CFO in order to enhance quality of initial population and improve local search, correspondingly. The work is motivated by the need to enhance the computational effectiveness through attainment of improved outcomes compared to previous popular solutions, to explore the features of different parameters of to seek some ideal solutions. The first stage entails solving of CVRP through setting a variety of values to tune parameters for the HCSCROCFO-3Opt proposed. Then initialization of algorithm CS, CRO, CFO parameters are accomplished through tuning parameters within a tuning cycle. Subsequently, a novel solution is swapped in a random manner through a levy flight within the central loop, followed by execution of the hybrid solution as well as new CRO, CFO and CS algorithm solutions, whose implementation is supposed to enhance results for the local 3-Opt. Ultimately, the most ideal solution for general hybrid model's solution space is identified, after which the solution that is best-suited for the CVRP purposes is presented. Within the standard CVRP cases, reported computational tests in large scale in the literature demonstrate the efficiency of presented approach.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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