Proposing an Advanced Trending-based Grey Wolf Optimizer for Single-objective Optimization Problems

optimization algorithms play a crucial role in solving complex problems in various domains. Single-objective optimization algorithms aim to discover the most optimal solution for a particular objective function, commonly distinguished by a single criterion or goal. Grey Wolf optimizer (GWO) is a swarm-based algorithm that has gained attention due to its simplicity and efficiency in solving optimization problems. In this article, we propose an advanced version of GWO, which is referred to as the Advanced Trending-based Grey Wolf optimizer (ATGWO), specifically tailored for single-objective optimization problems. The motivation behind this modification stems from the need to improve the performance metrics of the original GWO algorithm and avoid local optimum. By altering the algorithm’s coefficients, we aim to enhance its convergence rate, exploration, and exploitation abilities. To evaluate the proposed ATGWO algorithm, we conduct simulations using 7 multimodal single-objective benchmark functions. The results suggest that although the ATGWO excels in accuracy, it has more delay in comparison with GWO. This study paves the way for future research about optimization algorithms.