<p>With the increasing competition in the electricity market and the rising energy demand, unit commitment has become a critical and challenging task in power systems. Unit commitment is essential for effective planning and scheduling in modern power systems, contributing significantly to energy economics by reducing annual costs. This study employs the General Algebraic Modeling System (GAMS) using the Branch and Reduce Optimization Navigator (BARON) solver combined with the Monarch Butterfly Optimization (MBO) algorithm. It considers equality and inequality constraints of various units, system power balance, and losses. The BARON solver in GAMS and the MBO method addresses a range of units and system constraints to find optimal solutions. To evaluate the performance of these methods, simulations are conducted on three different systems: the IEEE 14-bus system with five generators, the IEEE 30-bus system with six generators, and the Uttar Pradesh 75-bus system with fifteen generators. Comparative analyses are performed by comparing the results from the BARON solver in GAMS with other available solvers in GAMS and the MBO approach. The significant percentage improvement of the proposed technique over the MBO method is seen in the higher test systems compared to small-scale test systems. The improvement in operating costs without and with transmission losses is 0.223% and 0.377%, respectively, for the 75-bus Uttar Pradesh utility data relative to the binary MBO approach. These findings indicate that BARON consistently surpasses other solvers and the MBO technique in minimizing total operating costs, ensuring robustness and constraint satisfaction levels. The practical implications of the research findings underscore the relevance and applicability of these methods in unit commitment optimization, providing a convincing and reassuring conclusion to this study.</p>