An Evolutionary Algorithmic Approach for Improving the Success Rate of Selective Assembly through a Novel EAUB Method

This work addresses an evolutionary algorithmic approach to reduce the surplus pieces in selective assembly increase success rates. A novel equal area amidst unequal bin numbers (EAUB) method is proposed for classifying parts of ball bearing by considering various tolerance ranges parts. The L16 orthogonal array used identifying effectiveness EAUB through varying number bins assembly. Because qualities such as minimal setting parameters, ease understanding and implementation, rapid convergence, moth–flame optimization (MFO) algorithm put forward this optimal combination toward maximizing percentage rate making assemblies. Computational results showed a 5.78% improvement compared with past literature. usage MFO justified comparing computational harmony search algorithm.