A Dual-Level Bio-Inspired Optimization Algorithm for Cloud Manufacturing Service Evaluation on Industrial Internet of Things (IIoT) Platforms
Bibliographic record
Abstract
Inspired by the immune-endocrine system, an improved biological comprehensive optimization algorithm (IBCOA) is proposed for industrial big data analysis and cloud manufacturing service matching. IBCOA employs a dual-level strategy: a bottom-level global optimization immune algorithm (GOIA) narrows down the search space to optimize short-term parameters, while a top-level fuzzy weighted comprehensive evaluation (FWCE) refines the solutions by incorporating long-term performance metrics. Experimental results demonstrate IBCOA’s superior performance, showing higher accuracy, recall, and F1 scores compared to least squares, decision trees, and<?A3B2 \break=""?> K-means clustering, along with longer execution time and lower error rates. When tested on standard benchmarks including Iris (classification), MNIST (handwritten digits), and CIFAR-10 (image recognition), IBCOA achieves remarkable accuracies, highlighting its strong generalization and adaptability. The algorithm not only addresses immediate production requirements in polyester fiber industrial data analysis but also enhances long-term operational efficiency and product quality. By balancing stakeholder interests (suppliers, consumers, operators), it promotes sustainable development on industrial internet platforms. This work provides a robust solution for the industrial Internet of Things (IIoT) service evaluation and classification tasks, demonstrating transformative potential for cloud manufacturing resource allocation across diverse applications.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".