An enhanced connected banking system optimizer with multiple strategies for numerical optimization problems

Connected Banking System Optimizer (CBSO) is a recently proposed meta-heuristic inspired by inter-bank financial transactions. Owing to its parameter-free nature, it has shown competitive performance on engineering constrained optimization problems. Nevertheless, the CBSO algorithm still suffers from limited inter-population information exchange and an insufficiently smooth transition between exploitation and exploration, which often leads to premature convergence due to inadequate coverage of the search space. To address these shortcomings, this paper presents an enhanced variant called ECBSO that integrates a feedback selection strategy, a regenerative population strategy, and a distribution estimation strategy. Comprehensive experiments were conducted on the CEC-2017 benchmark suite to evaluate ECBSO, encompassing parameter sensitivity analysis, ablation studies, and comparisons with various advanced variants. Statistical validation was performed using the Wilcoxon rank-sum test, Friedman test, and Nemenyi post-hoc test to confirm ECBSO’s superiority over competing algorithms. The experimental results demonstrate that ECBSO possesses high optimization efficacy and robustness, achieving average Friedman ranks of 2.103 (10D), 1.586 (30D), 1.828 (50D), and 2.103 (100D). Finally, ECBSO was applied to ten real-world engineering constrained optimization problems. The outcomes show that it not only solves practical problems effectively but also maintains remarkable stability, establishing ECBSO as an outstanding meta-heuristic variant.