Ring Toss Game-Based Optimization Algorithm for Solving Various Optimization Problems
Bibliographic record
Abstract
There are many optimization problems in different scientific disciplines that should be solved and optimized using appropriate techniques.Population-based optimization algorithms are one of the most widely used techniques to solve optimization problems.This paper is focused on presenting a new population-based optimization approach called Ring Toss Game-Based Optimization (RTGBO) algorithm.The main idea of RTGBO is to simulate the behaviour of players and rules of the ring toss game in the design of the proposed algorithm.The main feature of the proposed RTGBO algorithm is the lack of control parameters.Steps of implementing RTGBO are described in detail and the proposed algorithm is mathematically modeled.The ability of RTGBO to solve optimization problems is evaluated on a set of twenty-three standard objective functions.These functions are selected from three different groups including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal.The performance of RTGBO is also compared with eight other well-known optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Emperor Penguin Optimizer (EPO), Hide Objects Game Optimization (HOGO), and Shell Game Optimization (SGO).The results of optimization of objective functions of unimodal type indicate the high exploitation ability of RTGBO in solving optimization problems.On the other hand, the results of optimizing the multi-model type objective functions indicate the acceptable exploration ability of RTGBO.The results also confirm the superiority of the proposed RTGBO algorithm over mentioned optimization techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".