A New Self-Adaptive Hybrid Approach Based on History-Driven Methods for Improving Metaheuristics
Bibliographic record
Abstract
We propose a new hybrid approach, that we call History-driven Particle Swarm Optimization-Simulated Annealing (HdPSO-SA), to improve metaheuristics performance through collaboration and history-driven methods. Collaboration is per-formed using a Self-Adaptive Binary Space Partitioning tree (SA-BSP tree) to partition search space and guide the hybrid frame-work to the most promising sub-region of a given continuous problem to solve. The hybrid framework consists of three phases. In the first phase, the SA - BSP tree is applied in PSO to record essential information, create the landscape of fitness values, and partition the search space during exploration. The second phase consists of a smart controller to learn the SA-BSP maturity condition to balance exploration and exploitation through HdPSO and SA, respectively. The proposed smart controller determines the appropriate step (iteration) for switching from HdPSO to SA. In the third phase, the search space will be limited to only the most promising sub-region. Then, the information of the best solution (fitness value and position) will be given to SA to exploit the limited search space. The proposed HdPSO-SA is compared to several metaheuristics on ten well-known uni-modal and multimodal continuous optimization benchmarks. The results demonstrate the superiority of HdPSO-SA in returning a good quality solution while reducing the execution time.
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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 | 0.001 |
| 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 | 0.002 | 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".