As a crucial component in construction, piles find extensive use in transportation infrastructure. Drivability, a term commonly employed to gauge the ease of pile installation, encompasses various factors. To evaluate the drivability, various indexes, such as MCS (maximum compressive stresses), MTS (maximum tensile stresses) and BPF (blow per foot) are proposed. Despite the need for multiple indexes to evaluate pile drivability, these metrics often share underlying commonalities, as they collectively describe features of the hammer-pile-soil system comprehensively. Thus, leveraging multi-task learning techniques becomes advantageous for tackling the drivability prediction problem. This paper proposes two enhanced multi-task learning models improved from MLS-SVR (Multi-output least-squares support vector regression machines) and hybridized with metaheuristic algorithms. Based on 4072 pile installing samples, the models undergo development and hyperparameter optimization employing SA (Simulated Annealing) and YYPO (Yin-Yang-pair Optimization). Six statistical indexes are employed evaluate the accuracy of the models. The test results reveal the superiority of YYPO-MLS-SVR over SA-MLS-SVR. Furthermore, comparative analysis against single-task models from previous studies demonstrates the robust performance of hybridized MLS-SVR models, even when addressing multiple problems concurrently. This paper presents a novel approach for the multi-index evaluation of pile drivability.