Collaborative Hybrid Grey Wolf Optimizer: Uniting Synchrony and Asynchrony

The Grey Wolf Optimizer (GWO) is a relatively new metaheuristic approach that has shown promising results in solving continuous optimization problems. The Grey Wolf Optimizer (GWO) is a relatively new metaheuristic approach that has shown promising results in solving continuous optimization problems. However, it faces challenges when applied to various optimization formulations that require different exploration–exploitation rates. This chapter introduces a modified version of GWO called the Group-based Synchronous-Asynchronous Grey Wolf Optimizer (GSAGWO). GSAGWO divides the population into three groups, each containing an equal number of candidate solutions. The optimization process employs a control strategy that allows for adjusting the exploration–exploitation balance. Additionally, the algorithm incorporates a mechanism to enhance population diversity by exchanging elements among the groups. The performance of GSAGWO is evaluated using the CEC2017 benchmark and several real-world engineering problems. Comparative analyses with other state-of-the-art metaheuristic algorithms demonstrate the effectiveness and accuracy of the discussed technique.