Towards An Optimized Dragonfly Algorithm Using Hill Climbing Local Search To Tackle The Low Exploitation Problem

The Dragonfly Algorithm (DA), a swarm intelligence algorithm inspired by the behavior of dragonflies, has been recently proposed and it was found to have a higher performance as compared to other swarm intelligence algorithms. However, it still has certain limitations, and its performance can be further improved. DA has a low exploitation phase, and this leads to problems such as low accuracy of solutions, falling in local optima and low convergence rate. We propose to enhance the exploitation of DA by using the hill climbing algorithm as a local search so as to increase its effectiveness and efficiency in producing high accuracy solutions. The hill climbing algorithm is chosen to be used as a local search for DA since it always optimizes the current solution until the local optima is obtained However, it has not been employed in any existing hybrid of DA. The proposed algorithm performance is to be evaluated by employing it as a training algorithm for an Artificial Neural Network (ANN) to optimize its connection weights. A classification dataset will be used for the training and testing of the ANN trained by the proposed algorithm. The root mean squared error of the ANN will be taken as the objective function of the optimized dragonfly algorithm. The accuracy of the resultant neural network will be compared to that of an ANN trained by the original DA and the time taken for the training process by both algorithms will be compared. Based on the analysis, the optimized algorithm is expected to outperform the original DA by allowing the resultant neural network to have a higher classification accuracy.