Paper [12] presents a combination model based on a wavelet support vector machine (w-SVM) and a quantum fireworks algorithm (QFA) for icing prediction, and several real-world cases have been applied to verify the effectiveness and feasibility of the established QFA-w-SVM model. Additionally, paper [13] proposes using the enhanced fireworks algorithm for support vector machine parameters optimization, and the experiment results show that the enhanced fireworks algorithm has been proved to be very successful for support vector machine parameter optimization and is also superior to other swarm intelligence algorithms. In combination models, the optimal parameters of SVM can be obtained by optimizing calculation with the optimization algorithm, which could ensure higher prediction precision. However, only few researches have adequately considered the relationship between factors and icing thickness, most of the researches only put the fundamental meteorological factors (temperature, humidity, wind speed) into the established methods. In this way, this stream of methods may fail to consider various influential factors of icing forecasting. Therefore, some researchers have started to consider more impact factors and apply other methods to study the icing forecasting problem. Paper [14] proposes an ice accretion forecasting system (IAFS) which is based on a state-of-the-art, mesoscale, numerical weather prediction model, a precipitation type classifier, and an ice accretion model. Paper [15] studies the medium and long term icing thickness forecasting on transmission lines and presents a forecasting model based on the fuzzy Markov chain prediction; Paper [16] studies the icing thickness forecasting model by using fuzzy logic theory. Paper [17] establishes a forecasting model which is based on wavelet neural networks (WNN) and the continuous ant colony algorithm (CACA) to predict icing thickness. However, it has been often noticed in practice that increasing the factors to be considered in a model may degrade its performance, if the number of factors that is used to design the forecasting model is small relative to the icing thickness. In addition, when faced with a large amount of data of icing forecasting, the above models will still degrade their performance and cannot reach the expected forecasting accuracy. Thus the feature selection is important when faced with the problem of obtaining more useful influential factors and dealing with a large amount of data in the icing forecasting task. The term feature selection refers to the algorithms that identify and select the input feature set with respect to the target task. In recent years, swarm intelligence algorithms have been widely used in feature selection, for example, the genetic algorithm [18], firefly algorithm [19], artificial immune system [20], immune clonal selection algorithm [21], particle swarm optimization algorithm [22], ant colony optimization algorithm [23] and so on. These algorithms have shown their performance in the feature selection problem because they can find the optimal minimal subset at each iteration time. Additionally, it can be seen that the swarm intelligent algorithms are easy to be realized by computer programs compared with the mathematical programming approaches. Support vector machine (SVM), proposed by [PERSON], is based on statistical theory, and has been widely applied in the forecasting system, however, SVM is complex and difficult to calculate. For solving the emerged problem of SVM, the least square support vector machine (LSSVM) has been proposed to solve the complicated quadratic programming problem. To some extent, LSSVM is an extension of SVM, and it projects the input vectors into the high-dimensional space and constructs the optimal decision surface, then turns the inequality computation of SVM into equation calculation through the risk minimization principle; thus, reducing the complexity of the calculation and accelerating the operation speed.