In mode-division multiplexing (MDM) systems, the computational complexity of the multi-input multi-output (MIMO) equalization module is a critical obstacle to practical development. The step size μ and the number of taps K are key parameters in the equalization algorithm, influencing the performance of finite impulse response (FIR) equalizers, including convergence speed and output signal quality. To alleviate the computational burden of locating the optimal μ-K combination, we propose two ant colony optimization (ACO) -based MIMO equalization schemes: the fixed ACO-MIMO and the random ACO-MIMO, corresponding to two optimization strategies. These schemes expedite the initialization process of both parameters. Subsequently, we conduct experiments to evaluate their performance in a 3-mode recirculating-loop transmission system. Our findings demonstrate that, compared to conventional schemes, such as genetic algorithm (GA) and steepest descent algorithm (SDA), the proposed ACO-MIMO schemes significantly reduce the number of calls to the equalization algorithm for locating optimal μ-K combination by up to 42.74% and 80.63%, reducing the complexity of the whole MIMO equalization for MDM systems. And the resulting hit-rate P_hit for the optimal μ-K combination reaches up to 99.34%. Moreover, the ACO-MIMO schemes exhibit stable performance across different data collected from various round-trips, confirming the robust operation for the long-haul MDM transmission. Finally, we investigate the performance disparity between the two proposed ACO-MIMO schemes through bit-error-rate (BER) distribution, concluding that under a large dataset with various BER distributions, the performance of both schemes is essentially equivalent.