Self-Adaptive Spherical Search with Constrained Multi-Operator Differential Evolution (SASS-CMODE) for nonlinear programming problems
Bibliographic record
Abstract
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Metaheuristic algorithms for constrained optimiza- tion problems have become popular because of their ease of use and capability to obtain global solutions. However, these population-based algorithms can be computationally expensive and may suffer from low accuracy due to the difficulty in obtaining feasible points. We present a novel algorithm, re- ferred to as SASS-CMODE, by integrating a modified Improved Multi-Operator Differential Evolution (IMODE) algorithm with the Self-Adaptive Spherical Search (SASS) method. IMODE is modified to make it suitable for solving constrained problems, leading to a new algorithm termed Constrained Multi-Operator Differential Evolution (CMODE). SASS-CMODE is capable of achieving solutions with high feasibility rate and high accuracy by utilizing SASS to identify good feasible points and CMODE to achieve accurate solutions with fewer function evaluations. To evaluate its performance, we test SASS-CMODE to 57 engi- neering problems. The results demonstrate its superiority over other state-of-the-art optimization algorithms. SASS-CMODE is also employed to solve a constrained optimization problem on identifying optimal levels of non-pharmaceutical interventions to control an epidemic, showcasing its versatility and applicability in real-world scenarios.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".