An enhanced connected banking system optimizer incorporating triple mechanism for solving global optimization problems

Connected Banking System Optimizer (CBSO) is a recently proposed meta-heuristic inspired by inter-bank financial transactions. It models inter-bank transaction behaviors across four sequential stages, collectively balancing exploration and exploitation. When confronted with complex landscapes, however, CBSO exposes three critical weaknesses: limited global-search capacity, an abrupt phase switch that disrupts the exploitation-exploration balance, and a pronounced tendency toward premature stagnation. These shortcomings become more conspicuous as problem complexity rises, undermining the algorithm’s ability to locate the true optimum. To overcome these deficiencies, this paper presents an enhanced variant—ECBSO—which incorporates three complementary mechanisms: dominant group guidance strategy, guided learning strategy, and hybrid elite strategy. The ECBSO algorithm is comprehensively evaluated on the CEC 2017 benchmark suite and on real-world constrained engineering problems, outperforming CBSO, ISGTOA, EMTLBO, LSHADE, APSM-jSO, GLS-MPA, ESLPSO, ACGRIME, RDGMVO in all comparisons. Statistically, ECBSO secures first place across every test case, delivering Friedman ranks of 2.069, 2.138, 2.690, and 2.759, thereby confirming its superior convergence accuracy, search reliability, and optimization precision across diverse landscapes.