<![CDATA[This study investigates the effectiveness of a hybrid optimization algorithm that combines the Coyote Optimization Algorithm (COA) and the Water Cycle Algorithm (WCA), referred to as the COA-WCA algorithm, for optimizing Hybrid Renewable Energy Systems (HRES). The objective of this research is to evaluate the performance of the proposed COA-WCA algorithm in comparison with other non-hybrid algorithms, including COA, WCA, and the Whale Optimization Algorithm (WOA), in terms of convergence behavior, stability, robustness, and overall optimization performance. A multi-objective optimization approach is employed to minimize the Cost of Energy (COE), Loss of Power Supply Probability (LPSP), and Dummy Load (DL), which are the key factors in Hybrid Renewable Energy System (HRES) design. The COA-WCA algorithm demonstrates superior performance over other algorithms, such as its speed in finding the optimal solution and greater consistency in obtaining optimal solutions, particularly in complex and dynamic environments where renewable energy generation is characterized by high uncertainty. The results indicate that the COA-WCA effectively balances exploration and exploitation, ensuring efficient global search and improved local solution refinement. This study provides a significant contribution to the field of energy optimization by introducing a hybrid approach that enhances the efficiency and reliability of HRES.]]>
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