A New Metaheuristic Algorithm Called Treble Opposite Algorithm and Its Application to Solve Portfolio Selection
Bibliographic record
Abstract
This work presents a new metaheuristic called treble opposite algorithm (TOA).It consists of three phases.There are two searches that are opposite to each other performed in each phase.In the first phase, the search toward and away from the best solution is carried out.In the second phase, the search toward and away from the middle between two randomly picked solutions is carried out.In the third phase, a neighborhood search around the narrow and large space is carried out.A candidate is selected among the two searches in every phase.TOA is challenged to solve theoretical and practical problems.The 23 functions represent theoretical problems, while the portfolio optimization of stocks in the banking sector listed in IDX30 represents the practical problem.TOA is compared with five metaheuristics: grey wolf optimization (GWO), golden search optimization (GSO), average subtraction-based optimization (ASBO), zebra optimization algorithm (ZOA), and coati optimization algorithm (COA).The result indicates that TOA is superior to its competitors as it is better than GWO, GSO, ZOA, ASBO, and COA in 22,23,19,20,and 19 functions respectively, in handling 23 functions and produces the highest total capital gain in handling portfolio optimization problem.In the future, TOA can be utilized to handle many other realworld optimization problems.Moreover, TOA can be hybridized with other metaheuristics to improve its performance.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".