Cuckoo catfish optimizer: a new meta-heuristic optimization algorithm
Bibliographic record
Abstract
Abstract A new meta-heuristic algorithm, Cuckoo Catfish Optimizer (CCO), is proposed for numerical optimization problems. It simulates the search, predation, and parasitic behavior observed in cichlids. Early iterations of the algorithm focus on executing a multidimensional enveloping search strategy and a compressed space strategy, combined with an auxiliary search strategy to efectively limit the escape space of cichlids. This phase ensures extensive exploration of the solution space. In the intermediate stage of iteration, the algorithm uses a transition strategy to promote a smooth transition from exploration to exploitation, endowing the algorithm with both a certain degree of exploration capability and exploitation capability. In later stages, the algorithm uses chaotic predation mechanisms to create disturbances around cichlids to improve the exploitation of optimal solutions. Throughout the entire optimization process, the guidance, parasitism, and death mechanisms of individuals are integrated, allowing individuals to adjust their positions in real-time and improve the overall convergence accuracy. This paper rigorously evaluates the performance of CCO through 23 classic test functions and three CEC test suites. The experimental results show that compared with 11 famous algorithms and 10 novel improved algorithms, CCO can obtain the optimal solution in 91.52% of the test functions, demonstrating its excellent ability in solving various numerical optimization problems. Additionally, through the successful application to 6 mechanical optimization problems, 3 photovoltaic cell parameter optimization problems, and 1 path opti- mization problem, the competitiveness of CCO in solving real-world problems is verified and highlighted. The CCO source code can be downloaded here: https://ww2.mathworks.cn/matlabcentral/fileexchange/176828-cuckoo-catfish-optimizer-a-new-meta-heuristic-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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".