Performance Evaluation of Pdo Algorithm Through Benchmark Functions and Mlp Training

Abstract

Metaheuristic algorithms have become very common in the last two decades. The flexibility and ability to overcome obstacles in solving global problems have increased the use of metaheuristic algorithms. In the training of multilayer perceptron (MLP), metaheuristic algorithms have been preferred for many years due to their good classification capabilities and low error values. Therefore, this study evaluates the performance of the Prairie dog optimization (PDO) algorithm for MLP training. In this context, there are two main focuses in this study. The first one is to test the performance of the PDO algorithm through test functions and to compare it with different metaheuristic algorithms for demonstration of its superiority, and the second is to train MLP using the IRIS dataset with the PDO algorithm. As the PDO is one of the most recent metaheuristic algorithms, the lack of any study on this subject is the motivation for the article. PDO algorithm can be used in real-world problems as a powerful optimizer, as it reaches the minimum point in functions, and can also be used as a classification algorithm because it has successfully performed in MLP training.