Standard Detective Behavior Algorithm (DBA) may exhibit limited exploitation in later iterations, insufficient use of individual historical information, and unstable local search on complex continuous optimization problems. To address these limitations, this paper proposes a hybrid DBA with particle swarm optimization, denoted as DBA-PSO. The method retains the search-space-centered exploration, Lévy-flight-based local search, direct-attack refinement, and opposition-based learning mechanisms of DBA, while introducing the velocity update and personal-best/global-best guidance mechanisms of particle swarm optimization (PSO). At each iteration, DBA-PSO generates one DBA candidate and one PSO candidate and selects the next position through a fixed probabilistic fusion rule. Experiments on 23 classical benchmark functions and the CEC2017 test suite, together with supplementary CEC2022 and constrained engineering-design tests, show that DBA-PSO improves the mean fitness or stability on several functions compared with DBA or PSO and remains competitive on many test cases. Overall, the results support the usefulness of PSO-based velocity memory and historical-best guidance for improving the later-stage search behavior of DBA, while the mixed CEC2022 ranks, uneven engineering feasibility rates, and fixed fusion probability show that the method does not provide uniform dominance across all problem classes.

Quantifying the extent to which historical information can be borrowed remains challenging in the reliability assessment of aeronautical equipment with small-sample right-censored lifetime data. This paper proposes a Bayesian reliability assessment framework that incorporates historical information and uses simulation to calibrate prior-borrowing boundaries. A two-parameter Weibull model is adopted. Weakly informative, historical empirical, discounted, and fixed-mixture priors are specified in the log-parameter space, and posterior distributions are updated using the right-censored likelihood. Independent historical and current fleets are simulated to examine compatible historical information, scale shifts, and prior misspecification. Under compatibility, the historical empirical Bayes method yields relative errors of 2.52%, 3.54%, and 3.12% for the shape parameter, scale parameter, and mean lifetime, respectively, with a mean absolute error of 1.28% for the reliability function. Using the relative error of the scale parameter and the mean absolute error of the reliability function as joint criteria, historical-information borrowing is divided into full-borrowing, discounted-borrowing, and fixed-mixture correction regions, thereby establishing engineering decision boundaries. The thresholds are simulation-calibrated for the sample size, censoring proportion, and parameter settings considered. The framework quantifies reliability uncertainty and supports prior selection and risk calibration for comparable equipment.