A New Constrained Multiobjective Optimization Algorithm Based on Artificial Immune Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper proposes a new constrained multiobjective optimization algorithm based on artificial immune systems (AIS). To deal with constrained multiobjective optimization problems, the constrained AlS-based multiobjective optimization algorithm is developed by integrating a proposed constraint-handling technique with the unconstrained AIS-based multiobjective optimization algorithm named MOAIS (Xiao and Zu, 2006). We propose the constraint-handling technique by extending a single-objective constraint-handling technique called stochastic ranking (Runarsson and Yao, 2000) to multiobjective optimization process. Two scenarios of the multiobjective version of stochastic ranking are suggested. Thereafter, we develop the constrained MOAIS named MOAIS+SR by integrating the two scenarios with MOAIS. A comparative study is performed quantitatively to assess the performance of MOAIS+SR on a constrained test function suite called CTP test problems. In the comparative study, MOAIS+SR is compared against two other constrained multiobjective algorithms. The simulation results show that the proposed multiobjective stochastic ranking outperforms the constrained-dominance principle (Deb et al., 2000) in handling constraints. Furthermore, we show that the proposed MOAIS+SR achieves the best overall performance among the three algorithms under consideration on the CTP test problems. This study demonstrates that the proposed MOAIS+SR is highly competitive with other state-of-the-art algorithms in constrained multiobjective optimization.
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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