This paper, utilizing different evolutionary optimization algorithms, investigates on optimal economic sizing of a stand-alone (HRES) for a community in Kurukshetra, India. In the process of optimization, numbers of different subsystems viz. photovoltaic (PV), wind turbine (WT), battery, and diesel engine generator (DEG) are considered as variables of interest with the net present cost, payback period, computational cost, and levelized cost of energy (LCOE) as the performance measures. From analysis of the results, it is established that the solution provided by Whale optimization algorithm (WOA) turns out to be the best in terms of LCOE, net present cost, and also the payback period compared to the solutions provided by particle swarm optimization (PSO), Gravitational search algorithm (GSA), Grey wolf optimizer (GWO) and the combined PSO-GSA algorithms. The relative performance of these algorithms is compared and contrasted qualitatively as well as quantitatively highlighting the research findings not only in respect of optimal sizing of stand-alone HRES from economic perspective, as per the problem statement, but also in terms of their other performance measures such as convergence time, computational cost, and complexity. The simulations are executed in MATLAB software.