Interactive enhanced particle swarm optimization: A multi-objective reliability application
Bibliographic record
Abstract
In reliability optimization problems, it is desirable to address different conflicting objectives. This generally includes maximization of system reliability and minimization of cost, weight, and volume. The proposed algorithm of a metaheuristic nature is designed to address multi-objective problems. In the presented algorithm, interaction with a decision maker guides the search towards the preferred solution. A comparison between an existing solution and the newly generated solution substantiates the desirability or fitness of the latter. Further, the utility function expresses the preference information of the decision maker while searching for the best solution. During the development of the algorithm, a new variant of particle swarm optimization (PSO) is proposed and named as ‘enhanced particle swarm optimization’ (EPSO). EPSO considers the difference between the particle's best position and the global best position for efficient search and convergence. The developed algorithm is applied to the reliability optimization problem of a multistage mixed system with four different value functions that are used to simulate the designer's opinion in the solution evaluation process. Results indicate that the algorithm effectively captures the decision maker's preferences for different structures. Superior results in multi-objective reliability problem-solving prove the algorithm's superiority over other approaches.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".