Comparison Study of Discrete Optimization Problem Using Meta-Heuristic Approaches: A Case Study
Bibliographic record
Abstract
This paper presents the performance comparison of five meta-heuristic algorithms to solve a discrete optimization problem. The comparison is undertaken for a case of simply supported plate subjected to biaxial loading conditions. Furthermore, the optimization objective is to determine the optimal stacking sequence design of a laminate that maximizes the critical buckling load factor (λcb). The chosen meta-heuristics have been implemented using MATLAB with the same convergence criteria and the same maximum number of iterations to ensure a fair comparison. The implemented assessment criterion has performance measures of average CPU time, solution price, reliability, and normalized price. The results have demonstrated the outperformance of the Ant Colony Optimization Algorithm (ACOA) over other algorithms, which confirms the findings of previous studies. Moreover, the Tabu search algorithm (TS) and the Discrete Particle Swarm Optimization algorithm (DPSO) performed poorly due to their limited exploration capability. Additionally, the Genetic Algorithm (GA) and the Simulated Annealing algorithm (SA) exhibited a high level of reliability but showed an expensive solution cost. This study presents an adequate comparison approach of meta-heuristics, where it extends the comparison scope to cover the performance analysis of meta-heuristics more than that previously done in the domain of stacking sequence design optimization.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".