Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction
Bibliographic record
Abstract
The project aims at the design and development of six hybrid nature inspired algorithms based on Grey Wolf Optimization algorithm with Artificial Bee Colony Optimization algorithm (GWOABC), Moth Flame Optimization Algorithm with Ant Lion Optimization algorithm (MFOALO), Cuckoo Search Optimization algorithm with Fire Fly Optimization Algorithm(CSFFA), Multi-Verse Optimization algorithm with Particle Swarm Optimization Algorithm (MVOPSO), Grey Wolf Optimization algorithm with Whale Optimization Algorithm (GWOWOA), and Binary Bat Optimization Algorithm with Particle Swarm Optimization Algorithm(BATPSO). Hybrid optimizations assume the implementation of two or more algorithms for the same optimization problem. "Hybrid algorithm" does not refer to simply combining multiple algorithms to solve a different problem but rather many algorithms can be considered as combinations of simpler pieces. The hybrid approach combines algorithms that solve the same problem but differs in other characteristics notably performance. A hybrid optimization uses a heuristic to choose the best of these algorithms to apply in a given situation. The proposed hybrid algorithms are benchmarked using a set of 23 classical benchmark functions employed to test different characteristics of hybrid optimization algorithms. The results of the fitness functions prove that the proposed hybrid algorithms are able to produce better or more competitive output with respect to improved exploration, local optima avoidance, exploitation, and convergence. All these hybrid algorithms find superior optimal designs for quintessential engineering problems engaged, showcasing that these algorithms are capable of solving constrained complex problems with diverse search spaces. Optimization results demonstrate that all hybrid algorithms are very competitive compared to the state-of-the-art optimization methods and validated by fitness function. The hybrid algorithms are applied for optimal efficiency determination in various design challenges based on cantilever beam problem.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".