TIMBO: Three Influential Members Based Optimizer
Bibliographic record
Abstract
One of the most important and efficient methods in providing suitable solutions for various optimization problems is population-based optimization algorithms.The main contribution and innovation of this paper is to present a new optimization method called Three Influential Members Based Optimizer (TIMBO) which is used for implementation in solving optimization problems.The main idea in designing the proposed TIMBO is to use three important population members with the titles of best member, worst member, and member as mean population in updating the position of population members of the algorithm in the problem search space.The most important feature and advantage of the TIMBO is that it does not have any control parameters, which means that there is no need to control the parameter in this algorithm.TIMBO has been mathematically modeled for use in solving various optimization problems.The efficiency of the TIMBO is analyzed in order to provide suitable quasi-optimal solutions on a set of twenty-three standard objective functions of different types unimodal, high-dimensional multimodal, and fixed-dimensional.Evaluation of unimodal functions indicates the high exploitation power of the proposed TIMBO and evaluation of multimodal functions indicates the appropriate exploration power of the TIMBO.Also, the results obtained from the TIMBO are compared with the performance of eight other well-known optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimization (GWO), Grasshopper Optimisation Algorithm (GOA), Hide Object Game Optimizer (HOGO), and Flow Direction Algorithm (FDA).The results of optimization of standard objective functions indicate the high capability of the TIMBO in providing quasi-optimal solutions suitable for various optimization problems.In addition, analyzing and comparing the performance of the other eight optimization algorithms shows that the TIMBO has a more effective ability to solve optimization problems and is much more competitive.
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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 | 0.001 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".