*Result*: Exploring Performance Nuances of Computational Intelligence-Based Optimization Algorithms through Hydrological Benchmarking.

*With the rise of a new era marked by computational and artificial intelligence, it is imperative to undertake to critically evaluate these paradigms to assess their reliability. Computational intelligence (CI)-based optimization algorithms, in particular, have attracted considerable attention from both practical and theoretical perspectives. Water and hydrology-related disciplines have embraced this class of algorithms due to the flexibility and adaptability they offer. However, recent criticisms of their underlying structures have raised concerns about their effectiveness in solving real-world problems. This study investigates the reliability of CI-based optimization algorithms in addressing real-world challenges within hydrological engineering. To establish a baseline comparison, 19 CI-based optimization algorithms and a random-based pseudo algorithm were evaluated using a nonlinear Muskingum flood routing model. According to the statistical test conducted, a significant number of these algorithms perform below the baseline. This outcome highlights not only issues with specific algorithms but also broader concerns regarding how they are applied within water engineering contexts. Evidence suggests that overfitting algorithm parameters on benchmark examples has led to a substantial overestimation of their reliability in practical applications. Rather than simply adopting newer, more complex algorithms with an increasing number of parameters, it is more beneficial to assess the computational efficiency and task-specific compatibility of each algorithm. A thoughtful and critical approach to algorithm selection and application is essential for advancing robust and reliable solutions in water engineering. Practical Applications: Optimization algorithms inspired by artificial intelligence are increasingly used in water and hydrological engineering to improve decision making and system performance. However, many of these algorithms have only been tested in simplified, theoretical settings. This study puts 19 of these algorithms to the test using real-world flood data and a widely used river routing model. The results show that many commonly used algorithms underperform when applied to practical problems—some even performing worse than a simple random search. These findings raise important concerns about how these tools are selected and used in engineering practice. Overtuning algorithms for benchmarks may make them look more reliable than they truly are. For engineers and practitioners, the message is clear: more complexity does not always mean better results. Instead, selecting algorithms that match the task and evaluating their real-world performance is key. [ABSTRACT FROM AUTHOR]*