Enhanced Black Widow Algorithm for Numerical Functions Optimization
Why this work is in the frame
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Bibliographic record
Abstract
Black widow optimization algorithm is a recently evolutionary metaheuristic that imitates the unique mating behaviour of the black widow spiders in the real life. The trend of published papers utilizing the BWO algorithm is growing rapidly due to its efficiency in solving various engineering optimization problems. However, the BWO does not always perform as well as it should, and this is due to the random initialization of the spiders also the loss of good candidate solutions during the search. To remedy these problems, we propose in this paper a modified black widow optimization algorithm (MBWO) based on three ideas. First, an efficient initialization technique is adopted, which can guarantee starting the search with finest quality spiders and plays a significant role in determining an optimal or near-optimal solution. Second, the sexual cannibalism phase is modified to avoid the loss of high-quality solutions. Finally, an adaptive adjustment of crossover and mutation probabilities is presented to achieve a compromise between the diversification and intensification. Experiments are carried out on nineteen standard benchmark functions with different dimensions. The simulation results reveal that MBWO algorithm outperforms the original one also other metaheuristic algorithms in term of solution accuracy, global optimality, and the convergence speed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 it