DBA-PSO: MATLAB Implementation

MATLAB implementation of the DBA-PSO algorithm, including source code, benchmark functions, and example experiments.

Standard Detective Behavior Algorithm (DBA) may exhibit limited exploitation in lateriterations, insufficient use of individual historical information, and unstable local searchon complex continuous optimization problems. To address these limitations, this paperproposes a hybrid DBA with particle swarm optimization, denoted as DBA-PSO. Themethod retains the search-space-centered exploration, Levy-flight-based local search, direct-attack refinement, and opposition-based learning mechanisms of DBA, while introducingthe velocity update and personal-best/global-best guidance mechanisms of particle swarmoptimization (PSO). At each iteration, DBA-PSO generates one DBA candidate and onePSO candidate and selects the next position through a fixed probabilistic fusion rule.Experiments on 23 classical benchmark functions and the CEC2017 test suite, together withsupplementary CEC2022 and constrained engineering-design tests, show that DBA-PSOimproves the mean fitness or stability on several functions compared with DBA or PSO andremains 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 searchbehavior of DBA, while the mixed CEC2022 ranks, uneven engineering feasibility rates,and fixed fusion probability show that the method does not provide uniform dominanceacross all problem classes.