An improved grey wolf optimizer with flexible crossover and mutation for cluster task scheduling

Abstract

With the rapid advancement of cloud computing, task scheduling algorithms inspired by natural phenomena have become a research focal point. The grey wolf optimizer (GWO), known for its strong convergence and ease of implementation, has attracted considerable attention. This study introduces an adaptive approach, GWO with the crossover and mutation variant (GWO_C/M), to integrate crossover and mutation strategies and thereby enhance the flexibility and applicability of the GWO. Rather than offering a fixed model, GWO_C/M employs different combinations of crossover and mutation strategies to enhance the balance between exploration and exploitation, solving issues including center bias. Extensive comparisons with 13 state-of-the-art (SOTA) models across six benchmark scenarios showed that GWO_C/M performed robustly, achieving an 87.2% success rate on 41 out of 47 test functions. Moreover, implementing GWO_C/M in CloudSim simulations markedly improved key performance metrics, including total execution time, task completion time, and load balancing. Further validation using the Alibaba Cluster Trace V2018 dataset confirmed that GWO_C/M improved resource utilization and reduced maximum task completion time, indicating the proposed approach's substantial benefits for task scheduling and overall system efficiency in cloud environments.