Document Type: Original Article
computer engineering and information technology department, Razi university, Kermanshah, Iran
department of computer engineering, Razi university, Kermanshah, Iran
The metaheuristic optimization algorithms are the relatively new kinds of optimization algorithms that are widely used for difficult optimization problems in which the classic methods cannot be applied and are considered as known and very broad methods for crucial optimization problems. In this study, a new metaheuristic optimization algorithm is presented, which the main idea of this algorithm is extracted from a kind of motion in physics. This algorithm is proposed to obtain better results compared to other optimization algorithms in this field and to experience a new path for reaching the desirable point. Hence, after introducing the projectiles optimization (PRO) algorithm, in the first experiment, it is evaluated by the determined test functions of the IEEE congress on evolutionary computation (CEC) and compared with the known and powerful algorithms of this field. And, in the second try out, the performance of the PRO algorithm is measured in two practical applications, one for the training of the multi-layer perceptron (MLP) neural networks and the other for pattern recognition by Gaussian mixture modelling (GMM). The results of these comparisons are presented in various tables and figures. Based on the presented results, the accuracy and performance of the PRO algorithm are much higher than other algorithms.