Diesel or other fossil fuel-based generation systems are typically used to meet electricity demand in remote and island areas. Nonetheless, due to the rising cost of fossil fuels and their harmful emissions, there is a growing trend toward the use of standalone hybrid renewable energy systems (HRESs). Hybrid systems with solar and wind energy have become a popular choice in such applications due to their complementary characteristics, matured technologies, and availability in most areas. However, the challenges associated with solar and wind energy systems are intermittency and high net present cost. In this context, optimal sizing is critical for achieving a reliable supply at a low cost via these standalone systems. As a result, there has been an increase in interest in developing algorithms for size optimization in standalone HRESs. So far, optimal sizing methodologies have been broadly classified as classical algorithms, modern techniques, and software tools. Because of their ability to solve complex optimization problems, modern techniques based on single artificial intelligence algorithms (AI) are becoming more popular than classical algorithms. Furthermore, there has been a clear trend in recent years toward the use of hybrid algorithms over single algorithms, owing to their ability to provide more promising optimization results. The purpose of this paper is to provide a comprehensive review of recent developments in size optimization methodologies, as well as an essential comparison of single algorithms, hybrid algorithms, and software tools used for sizing standalone solar and wind HRES. Furthermore, an evaluation of all possible combinations of standalone solar and wind energy systems, including their economic, reliability, environmental, and social assessment parameters, is presented