Chinese Bureaucratic System Algorithm: A Rank-Weighted Institutional Search Framework for Continuous and Routing Decision Systems

Many decision systems require search over continuous parameters or discrete route structures. This study examines whether a rank-weighted population architecture can support both representations while retaining an interpretable decision mechanism. We propose the Chinese Bureaucratic System Algorithm (CBSA), which transforms objective-based population ranks into either continuous movement or edge-based routing probabilities. We expect rank aggregation to be most useful when higher-ranked solutions contain stable information that can be reused in later searches. The framework is instantiated as a continuous variant (CBSA-C) and a discrete routing variant (CBSA-D), and its computational complexity and limiting search properties are analyzed. On the 2014 Congress on Evolutionary Computation (CEC2014) benchmark suite, CBSA-C obtains the best average rank among five algorithms and is strongest on multimodal and hybrid functions. On 50 capacitated vehicle routing instances, CBSA-D ranks second behind iterated local search while outperforming four population- or edge-based baselines. Across all 56 Solomon time-window routing instances, it is most competitive on clustered classes and weaker on random and mixed classes. These results support a conditional interpretation: rank-weighted aggregation is useful when objective ranks are informative and the representation contains reusable structure, while stronger local-search methods remain preferable when such structure is absent.