Abstract This study presents the implementation of a recently proposed meta-heuristic algorithm, the Starfish Optimization Algorithm (SFOA), to solve the thermal unit optimization operation (TUOO) problem. The main task of the entire study is to reach the minimum value of the entire fuel cost of all the generating units (GUs) in the given power system with a load demand of 2650 MW. In an effort to mitigate the negative environmental impacts of s and reduce the burden on all TGs, a 100MW solar-powered generator (SPG) and a 150MW wind- powered generator (WPG) have been added to the system. SFOA is applied to determine the optimal allocation of all GUs, taking into account prohibited operating zones (POZs). To verify the actual performance of SFOA, HO is also implemented to solve the given problem using the same initial parameters in terms of population size and maximum number of iterations, which are selected optimally through different settings. The results clearly indicate that SFOA not only achieves the lowest entire fuel cost (LEFC) of the main objective functions but also outperforms HO across the remaining comparison criteria, such as the average entire fuel cost (AEFC), maximum entire fuel cost (MEFC), and especially standard deviation (STD). Based on the achievements and results mentioned, SFOA is strongly recommended for solving the TUOO problem.