The fireworks algorithm, firstly proposed by [PERSON] and [PERSON] [24; 25; 26] in 2010, is a brand new swarm intelligence optimization algorithm. It is mainly a simulation of the process of a fireworks explosion. Due to its excellent performance, the fireworks algorithm has been widely used. For example, paper [27] applies the fireworks algorithm to solve the typical 0/1 knapsack problem. Additionally, paper [28] attempts to use the fireworks algorithm to solve nonlinear equation problems, and the experimental results show that the proposed algorithm has an obvious advantage in solving complex nonlinear equations with a large number of variables and high coupling of variables. In paper [29], the fireworks algorithm is applied in the identification of radioisotope signature patterns in the gamma-ray spectrum. Through simulated and real world experiments for gamma-ray spectra, the results demonstrate the potentiality of the fireworks algorithm has a higher accuracy and similar precision to that of multiple linear regression fitting and the genetic algorithm for radioisotope signature pattern identification. Paper [30] has made some improvement to the basic fireworks algorithm, and proposes a new culture fireworks algorithm to solve the digital filter design problem, and the simulations results show that the finite impulse response digital filter and infinite impulse response digital filters based on the culture fireworks algorithm are superior to previous filters based on particle swarm optimization, quantum behaved particle swarm optimization, and adaptive quantum behaved particle swarm optimization in convergence speed and optimization results. The fireworks algorithm has achieved excellent performances in real-world applications, however, few researches have attempted to apply the fireworks algorithm into the feature selection, especially in the field of icing forecasting of transmission lines. Through the above analysis, a new idea which can improve the accuracy of icing forecasting is presented. A new model of using the weighted least square support vector machine and the fireworks algorithm is established for icing forecasting. Firstly, the information redundancy and the influence of the noise on the accuracy of ice forecasting can be reduced by the fireworks algorithm (FA) for the feature selection, and more useful influence factors can be obtained to provide the appropriate input vector for the icing forecasting model. Secondly, by weighting the LSSVM through horizontal input vectors and a vertical training data set, the generalization ability and nonlinear mapping ability of the LSSVM algorithm can be improved, which can guarantee a higher fitting accuracy in training and learning. Thirdly, the parameters of weighted LSSVM can be optimized by the fireworks algorithm, in order to avoid the subjectivity of parameters selection. Finally, the established icing forecasting model in this paper will be used to solve the problem of icing forecasting with a large amount of data and many influence factors. In addition, this paper and the published paper (reference [12]) seem to be slightly similar, in that both of the papers are used for the icing forecasting of transmission lines. However, their models have many differences, mainly as follows: (1) The regression models used for icing forecasting of the two papers are different. This paper presents a new Weighted Least Square Support Vector Machine (W-LSSVM) to improve the mapping and fitting ability of LSSVM. However, the published paper proposed the Wavelet Support Vector Machine (w-SVM). The calculation processes of the two regression models are essentially different; (2) The application of the Fireworks Algorithm (FA) in the two papers is not the same. The FA in this paper is not only applied to the feature selection, but also used to optimize the parameters of W-LSSVM.