An Enhanced Hiking Optimization Algorithm for Cloud Computing Task Scheduling

Intelligent optimization algorithm is the key technology to solve the problem of cloud computing task scheduling. However, with the continuous expansion of data scale and the increasing complexity of scheduling problems, the performance of existing algorithms is severely restricted. To address this challenge, researchers have come up with a variety of algorithms. The Hiking Optimization Algorithm has been widely used in many fields. However, when dealing with cloud computing task scheduling, HOA still has some problems, such as long system response time and unbalanced computing resource load. In this paper, an improved HOA algorithm integrating multiple strategies is proposed. Specifically, by introducing an iterative chaotic mapping mechanism, the initialization process of the algorithm is optimized, which significantly improves the diversity of the population. On this basis, the S-shaped transfer function is further introduced to enhance the rationality and adaptability of the scheduling strategy. Finally, the task scheduling experiment proves that MHOA shows strong robustness and optimization performance, which can effectively solve the cloud computing task scheduling problem.