Ring Toss Game-Based Optimization Algorithm for Solving Various Optimization Problems
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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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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