A multi-population-based marine predators algorithm to train artificial neural network

Abstract

Marine predators algorithm (MPA) is one of the recently proposed metaheuristic algorithms. In the MPA, position update mechanisms are implemented, emphasizing global search in the first part of the search process, balanced search in the middle, and local search in the last part. This may adversely affect the local search capability of the algorithm in the first part of the search process and the global search capability in the last part of the search process. To overcome these issues, an algorithm called MultiPopMPA with a multi-population and multi-search strategy is proposed in this study. Thanks to the proposed algorithm, local, balanced, and global search strategies of the original MPA were utilized from the beginning to the end of the search process. Thus, it is aimed to contribute to a more detailed search of the parameter space. In this study, the proposed algorithm has been applied in training artificial neural networks for 21 different classification datasets. The success of the algorithm has been scored on precision, sensitivity, specificity, and F1-score metrics and compared with eight different metaheuristic algorithms, including the original MPA. In terms of the mean rank of success, the proposed MultiPopMPA has been ranked first in precision, sensitivity, and F1-score metrics and ranked second in the specificity metric. In addition, it has been observed that the proposed algorithm outperforms its competitors in most cases in terms of convergence and stability. Finally, Wilcoxon’s signed-rank test results calculated through the MSE metric showed that the proposed algorithm produced statistically significant results in most cases.