Development of a Novel Artificial Intelligence Model for Better Balancing Exploration and Exploitation

Abstract

Grey Wolf optimizer (GWO) has been used in several fields of research. The main advantages of this algorithm are its simplicity, little controlling parameter, and adaptive exploratory behavior. However, similar to other metaheuristic algorithms, the GWO algorithm has several limitations. The main drawback of the GWO algorithm is its low capability to handle a multimodal search landscape. This drawback occurs because the alpha, beta, and gamma wolves tend to converge to the same solution. This paper presents HDGM – a novel hybrid optimization model of dragonfly algorithm and grey wolf optimizer, aiming to overcome the disadvantages of GWO algorithm. Dragonfly algorithm (DA) is combined with GWO in this study because DA has superior exploration ability, which allows it to search in promising areas in the search space. To verify the solution quality and performance of the HDGM algorithm, we used twenty-three test functions to compare the proposed model’s performance with that of the GWO, DA, particle swam optimization (PSO) and ant lion optimization (ALO). The results show that the hybrid algorithm provides more competitive results than the other variants in terms of solution quality, stability, and capacity to discover the global optimum.