The increasing demand for cost-effective and efficient renewable energy solutions presents significant optimization challenges in hybrid energy systems. This paper addresses these challenges by conducting a comparative analysis of three advanced optimization algorithms—Lévy Flight Optimization (LFO), Archimedean Optimization (AO), and Quantum Gorilla Optimization (QGO)—to minimize the Total Net Present Cost (TNPC) and Levelized Cost of Energy (LCOE) in hybrid renewable energy systems. The study integrates critical cost parameters such as Capital Expenditure (CAPEX), Operational Expenditure (OPEX), replacement costs, and salvage values into an advanced optimization framework. Three system configurations are evaluated: Wind Turbines and Fuel Cells (WT/FC), Photovoltaic Systems and Fuel Cells (PV/FC), and a combined system (PV/WT/FC), under varying availability levels (100%, 96%, and 92%). The results demonstrate that LFO consistently outperforms the other algorithms, achieving the lowest TNPC of $0.051 for the WT/FC system at 96% availability, compared to $0.719 using QGO. These findings underscore the importance of selecting tailored optimization strategies to balance cost, performance, and system reliability. This research provides valuable insights for designing efficient and economically viable renewable energy systems, particularly for applications requiring consistent high energy output, such as monocrystalline and polycrystalline PV-based configurations.