Journal of Bionic Engineering (2025) 22:1506–1534https://doi.org/10.1007/s42235-025-00674-z

Multi-strategy Enhanced Hiking Optimization Algorithm for Task Scheduling in the Cloud Environment 

Libang Wu1  · Shaobo Li2,3 · Fengbin Wu2  · Rongxiang Xie1  · Panliang Yuan2

1 State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China 

2 State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China 

3 Guizhou Institute of Technology, Guiyang 550025, China

Abstract 

Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been proposed. The Hiking Optimization Algorithm (HOA) have been used in multiple fields. However, HOA suffers from local optimization, slow convergence, and low efficiency of late iteration search when solving cloud task scheduling problems. Thus, this paper proposes an improved HOA called CMOHOA. It collaborates with multi-strategy to improve HOA. Specifically, Chebyshev chaos is introduced to increase population diversity. Then, a hybrid speed update strategy is designed to enhance convergence speed. Meanwhile, an adversarial learning strategy is introduced to enhance the search capability in the late iteration. Different scenarios of scheduling problems are used to test the CMOHOA’s performance. First, CMOHOA was used to solve basic cloud computing task scheduling problems, and the results showed that it reduced the average total cost by 10% or more. Secondly, CMOHOA has been applied to edge fog cloud scheduling problems, and the results show that it reduces the average total scheduling cost by 2% or more. Finally, CMOHOA reduced the average total cost by 7% or more in scheduling problems for information transmission. 

Keywords Task scheduling · Chebyshev chaos · Hybrid speed update strategy · Metaheuristic algorithms · The Hiking Optimization Algorithm (HOA)