In this paper, a novel nature-inspired population-based discrete optimization technique, namely binary bat algorithm (BBA), is proposed for the first time to solve load frequency control (LFC) problem in power system. Initially, bat algorithm (BA) is designed by imitating the echolocation behavior of bats in nature and then binary approach has been hybridized to accelerate the convergence rate. To demonstrate the effectiveness of BBA, two nonlinear test systems, namely three-area and four-area hydrothermal power plants, are considered for design and analysis. To damp out frequency and tie-line power oscillations due to sudden load perturbation, optimized proportional-integral-derivative (PID)-type multi-input–single-output (MISO) static synchronous series compensator (SSSC) is designed and integrated in LFC loop. The controller gains are concurrently optimized by BBA employing integral square error (ISE)- and integral time absolute error (ITAE)-based fitness functions. The potency of the proposed scheme is proven among other well-known evolutionary techniques and intelligent controllers like fuzzy logic, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) by transient analysis method. The simulation results presented in this article clearly show that assimilation of BBA-tuned MISO-type PID-SSSC controller in LFC loop can curtail the system oscillations actively by contributing fast compensation. Finally, robustness of the designed controller is validated against random load perturbation (RLP).