Football Game Based Optimization: an Application to Solve Energy Commitment Problem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Heuristic optimization algorithms are widely used to solve problems in different fields of science. In this paper, a new game based optimization method called football game based optimization (FGBO) is presented which simulates the game of football. The population of FGBO are clubs and the variables of the problem are the players belonging to the clubs. FGBO has four phases: a) league holding, b) player transfer, c) practice, and d) promotion and relegation. The power of FGBO in solving optimization problems has been investigated on several benchmark test functions. The result of FGBO and other algorithm are obtained from implantation of these algorithms on unimodal, multimodal, and fixed-dimension multimodal benchmark test functions. Eight optimization algorithms called Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching Learning Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO) have been used to compare these results. The proposed FGBO algorithm is also used to solve the energy commitment (EC) problem. Based on the simulation studies and obtained results, FGBO has a higher efficiency than a number of other algorithms. The results and data obtained from applying FGBO and other mentioned algorithms on unimodal test functions, multimodal test functions, and energy commitment problem show that FGBO is able to provide better results in comparison with other well-known optimization algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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