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

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.