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Peer-reviewedCC BYBiomimetics (Basel, Switzerland)

A Dual-Strategy Cuckoo Catfish Optimizer for Continuous Optimization with Application to Wastewater Treatment.

  • Zongkun Li1,2,3,4
  • Shanfa Tang1,2,3,4
4 affiliations
  1. 1School of Petroleum Engineering, Yangtze University, Wuhan 430100, China.
  2. 2Hubei Key Laboratory of Oil and Gas Drilling and Production Engineering, Yangtze University, Wuhan 430100, China.
  3. 3National Engineering Research Center for Oil & Gas Drilling and Completion Technology, Yangtze University, Wuhan 430100, China.
  4. 4State Key Laboratory of Low Carbon Catalysis and Carbon Dioxide Utilization, Wuhan 430100, China.
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Abstract

Candidate generation in population-based optimization depends on both the starting population and the information exchanged during search. This study proposes the dual-strategy enhanced cuckoo catfish optimizer (DCCO), which modifies these two stages within the cuckoo catfish optimizer (CCO). Clustering-based Cauchy sampling generates approximately half of the initial population around geometric reference centers, while uniform sampling supplies the remainder. Horizontal and normalized-coordinate vertical crossover then recombine individuals and coordinates after each CCO population sweep. On 29 CEC2017 functions at D=30, DCCO achieves an average rank of 1.7586, the lowest among the nine compared methods. Component comparisons and parameter analysis characterize the integrated configuration. In a static wastewater operational model, the mean operating cost index is 22.63% and 24.64% lower than that of the swift flight optimizer (SFO) and CCO, respectively, under dry conditions. The selected decisions remain feasible under dry and rain loads, while all violate the storm constraints, motivating scenario-constrained decisions. The study evaluates the quality-computation trade-off of the integrated search design and its cost-feasibility behavior within the stated algebraic model.

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Cite

BibTeXRIS
BibTeX
@article{li2026dualstrategy,
  title = {{A Dual-Strategy Cuckoo Catfish Optimizer for Continuous Optimization with Application to Wastewater Treatment.}},
  author = {Zongkun Li and Shanfa Tang},
  journal = {Biomimetics (Basel, Switzerland)},
  year = {2026},
  month = oct,
  doi = {10.3390/biomimetics11100736},
  url = {https://europepmc.org/article/MED/42930288},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - A Dual-Strategy Cuckoo Catfish Optimizer for Continuous Optimization with Application to Wastewater Treatment.
AU  - Zongkun Li
AU  - Shanfa Tang
PY  - 2026
DA  - 2026/10/14
JO  - Biomimetics (Basel, Switzerland)
DO  - 10.3390/biomimetics11100736
UR  - https://europepmc.org/article/MED/42930288
AB  - Candidate generation in population-based optimization depends on both the starting population and the information exchanged during search. This study proposes the dual-strategy enhanced cuckoo catfish optimizer (DCCO), which modifies these two stages within the cuckoo catfish optimizer (CCO). Clustering-based Cauchy sampling generates approximately half of the initial population around geometric reference centers, while uniform sampling supplies the remainder. Horizontal and normalized-coordinate vertical crossover then recombine individuals and coordinates after each CCO population sweep. On 29 CEC2017 functions at D=30, DCCO achieves an average rank of 1.7586, the lowest among the nine compared methods. Component comparisons and parameter analysis characterize the integrated configuration. In a static wastewater operational model, the mean operating cost index is 22.63% and 24.64% lower than that of the swift flight optimizer (SFO) and CCO, respectively, under dry conditions. The selected decisions remain feasible under dry and rain loads, while all violate the storm constraints, motivating scenario-constrained decisions. The study evaluates the quality-computation trade-off of the integrated search design and its cost-feasibility behavior within the stated algebraic model.
SN  - 2313-7673
ER  -