Application of Crayfish Optimization Algorithm for Addressing Combined Heat and Power Dispatch Problem
Bibliographic record
Abstract
The Crayfish Optimization Algorithm (COA), a bio-inspired optimization approach, is presented in this paper to handle the Combined Heat and Power Dispatch (CHPD) problem with confined viable operating zones. The exploration and exploitation ability is mainly governed by the three stages known as the summer resort stage, competition stage, and foraging stage. Crayfish mostly change their location based on the temperature. Three CHPD case studies are used to validate the performance of the proposed method. Based on the findings, COA reduced generation costs by 2.14%, 2.87%, and 7.12% when compared to the prior best values obtained by CPSO and OTLBO. COA outperforms the other optimization techniques, as shown by the comparative analysis against Particle Swarm Optimization (PSO), Classical PSO (CPSO), Time-Varying Acceleration Coefficients PSO (TVAC-PSO), Teaching-Learning-Based Optimization (TLBO), and Oppositional TLBO (OLTBO). The outcomes demonstrate that COA outperforms these approaches regarding cost savings, computational efficiency, and solution accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".