Economic Load Dispatch Solutions at Small, Medium, and Large Scales Utilizing Chaotic Spotted Hyena Optimization
Bibliographic record
Abstract
Incorporating ideas from both spotted hyenas and chaotic functions, the newly developed chaotic spotted hyena optimization (CSHO) is a meta-heuristic search algorithm that takes its cues from the natural hierarchy and hunting strategy of hyenas. Prey is either (a) actively sought after and pursued, (b) deemed too difficult to approach, (c) causing problems and encircled, or (d) rendered immobilized before being attacked. In this work, we demonstrate how the CSHO method may be used to solve the economic load dispatch problem (ELDP) for a power grid that is both non-convex and subject to dynamic changes. Comparisons are made to other algorithms like particle swarm optimization, ant lion optimizer, complex algorithm (CM), enhanced swift converging simulated annealing (ESCSA), hybrid many-objective particle swarm optimization (HMAPSO), parallel particle swarm optimization (PPSO), multi objective particle swarm optimization (MPPSO), discrete particle swarm optimization (DPSO), adaptive phasor particle swarm optimization (APPSO), k-Logic, and spherical vector-based particle swarm optimization (SPSO) to validate the findings, which show that CSHO performs well for ELDP in systems of various sizes. In both the absence and presence of transmission losses, the CSHO and traditional approaches are used to find optimal solutions to the economic load dispatch issue for the 3-, 6-, 10-, 13-, 38-, 40- (including valve point loading), and 140-unit systems, respectively. The CSHO algorithm is shown to be capable of producing competitive outcomes in a variety of benchmarking exercises, against the backdrop of various well-known traditional, heuristic, and meta-heuristic search algorithms.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".