Competing leaders grey wolf optimizer and its application for training multi-layer perceptron classifier

The multi-layer perceptron (MLP) is a highly popular artificial neural network used for classification across various applications. Swarm intelligence algorithms, such as the grey wolf optimizer, battle royale optimizer, sine cosine algorithm, and their improved variants, have been widely utilized for training MLP classifiers, addressing the challenging optimization problem that covers various difficulties in determining the optimal network parameters to minimize errors while processing datasets. Nevertheless, these prevailing algorithms frequently encounter problems like stagnation in local optima and inefficient search, leading to a deficiency in their ability to deliver efficient and accurate solutions. In order to overcome this challenge and provide a powerful tool for MLP classifier training, the competing leaders grey wolf optimizer (CGWO) is proposed in this study. CGWO benefits from a novel mechanism of competing leaders that provides a flexible wolf pack leadership hierarchy to avoid stagnation in local optima and accelerate convergence speed. In addition, a population diversity-enhanced initiation method is designed to help improve the efficiency of the mechanism of competing leaders. The performance of CGWO in training MLP classifiers is evaluated in an experiment comparing CGWO with nine state-of-the-art algorithms on eleven classification datasets representing a wide range of optimization challenges. CGWO substantially outperforms its competitors regarding optimal fitness, classification accuracy, and convergence speed; and shows competitive performance in computation time comparison.