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• Provide a solution for power generation, especially in rural areas known for the non-availability of conventional sources such as oil or coal. • Propose a hybrid renewable energy system based on a PV panel, a wind turbine, a battery, a fuel cell, an Electrolyzer, and a hydrogen tank. • Utilize a reliability and an economic criteria to determine the right sizing of the different system elements. • Investigate the intelligent optimization methods (PSO, CSA, GWO, and SSA) in the optimal sizing of these elements given their various advantages in many applications This research introduces a constrained multi-objective salp swarm optimization (SSA) technique for the optimal sizing of a stand-alone hybrid renewable energy system (HRES). The latter comprises a photovoltaic power source, a wind power source, a fuel cell, an electrolyzer, a battery, and a hydrogen tank. A precise assessment of these compounds is essential, as it influences system reliability and costs. The considered system has been used to constantly provide for three distinct types of load demands (educational building, a hotal, and mechanical workshop). The optimization process is based on minimizing two objective functions, which are a reliability function known as the Deficiency of Power Supply Probability (DPSP) and economic functions denoted Total Net Present Cost (TNPC) and Energy Cost (EC). The efficiency of the considered algorithm has been assessed by comparing it to the particle swarm optimization (PSO) algorithm, the cuckoo search algorithm (CSA), and the grey wolf optimization (GWO) algorithm. The outcomes of the simulation displayed that the SSA outperformed the other algorithms in proposing the most reliable and cost-effective sizing of the system to satisfy the load demands with a DPSP of 0%, a TNPC of 327694 US$, and a EC of 0.9541 US$/kWh for the educational building, a DPSP of 0%, a TNPC of 8 717 400 US$, and a EC of 0.8981 US$/kWh, for the hotel and a DPSP of 0%, a TNPC of 98 590 US$, and a EC of 1.3443 US$/kWh for the mechanical workshop.