###### Abstract
From an engineering point of view, a good problem model should reflect the real environment as much as possible, so that the model can be used in practical and commercial applications. From the computational science perspective, the algorithm that is used to solve an engineering problem should not only fully integrate with the problem model, but should be computationally efficient as well. As discussed in Section 3, many of the studies proposed an efficient problem model, but the computational model was not effective. In other  
Figure 1: Growth of wind energy installed capacity: 2000–2019. studies, the case was the opposite. Therefore, there is a need to have an approach that provides both an effective problem model and computational efficiency. As far as the problem model is concerned, the turbine selection problem should ideally be modeled as a multi-criteria decision-making (MCDM) problem, where several decision factors should be considered in the turbine selection process, for a more realistic and practical problem design. For example, the fluctuations in the wind speed negatively affect the amount of energy generated, and therefore, the selection of a turbine should consider the wind speed as a decision factor. Furthermore, the variations in wind speed depend on several factors, such as geographical location, climate, topography, and turbine hub height. While the first three factors depend on the natural conditions of the location and cannot be changed, the turbine height is a user controlled variable. This turbine height is directly related to the wind speed, which in turn affects several other factors, such as percentage of zero output, percentage of rated output, and annual energy production. Percentage of zero output refers to the percentage of time for which a turbine does not produce any energy. Likewise, percentage of rated output refers to the percentage of time during which a turbine produces maximal energy. The annual energy production refers to the total energy produced by a turbine over a period of one year. Studies have shown that the hub height (the height of tower on which a turbine is installed) is crucial for the amount of energy generated from a turbine [21; 31]. The reason is that wind speed is different at different heights, and more wind is available at higher altitudes, thereby resulting in higher energy yields [31]. However, hub height cannot go beyond a certain limit due to technical, financial, structural, and operational issues. Studies indicate that increasing the hub height just by 10 m results in an overall cost increment by of around USD 120,000 [21]. Therefore, it is not possible to optimize hub height, wind speed, percentages of zero power and rated power, and annual energy output at the same time due to the conflict among them. Accordingly, the problem model desires an approach which can effectively provide a balance between the aforesaid factors. This balance can be achieved by utilizing multi-criteria decision-making (MCDM) techniques. Over the years, several MCDM techniques have been proposed in the literature. One such technique is the technique for order of preference by similarity to ideal solution, commonly known as TOPSIS. The approach has been successfully applied to a number of MCDM problems in several disciplines. In addition, the TOPSIS approach provides a simple yet effective mechanism to deal with multiple criteria, in addition to being computationally efficient. These observations motivated the use of the TOPSIS technique for the wind turbine selection problem that is modeled as an MCDM problem in this paper. The paper demonstrates the application of the proposed novel method based on TOPSIS for data sets from Saudi Arabia. However, this method can be used comfortably with any data set from anywhere. Thus, the proposed method is not localized, but rather applied to a local problem. A detailed discussion on the novelty of the method, based on the published work, has been given in Section 3. The rest of this paper is organized as follows.