An ABC-Genetic method to solve resource constrained project scheduling problem
Bibliographic record
Abstract
The aim of this work is to study the effect of hybridization on the performance of the Artificial Bee Colony (ABC) as arecently introduced metaheuristic for solving Resource Constrained Project Scheduling Problem (RCPSP). For thispurpose the ABC is combined with the Genetic Algorithm (GA). At the initial time, the algorithm generates a set ofschedules randomly. The initial solution is evaluated against constraints and the infeasible solutions are resolved tofeasible ones. Then, the initial schedules will be improved iteratively using hybrid method until termination condition ismet. The proposed method works by interleaving the ABC and GA search processes. The GA method updates schedulesby considering the best solution found by the ABC approach. Next the ABC approach picks the solutions found by GAsearch. A new approach is used by the algorithm to maintain the priorities of the activities in feasible ranges. Theperformance of the proposed algorithm is compared against a set of state-of-art algorithms. The simulation results showedthat the proposed algorithm provides an efficient way for solving RCPSP and produce competitive results compared toother algorithms investigated in this work.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".