An Enhanced Seahorse Optimizer Using Alpha Balance Factor and t‐Distribution Mutation for Task Scheduling in Cloud Computing

ABSTRACT Cloud computing system task scheduling optimization has garnered substantial attention because it directly influences resource utilization, service quality, and system energy consumption. To address the demands of cloud computing environments, an enhanced seahorse optimizer using alpha balance factor and t‐distribution mutation is proposed. Firstly, the alpha balance adaptive mechanism was proposed. The original alpha balance factor exhibits a relatively smooth downward trend. To overcome this limitation, the standard normal distribution random numbers are considered to add, which introduces greater volatility and enhances the algorithm's ability to jump out of the local optima. Secondly, a multi‐dimensional t‐distribution mutation operator was designed, taking into account both exploration and exploitation. This promotes the dynamic exploration and exploitation of seahorse individuals during the movement behavior stage, enhances population diversity, reduces the possibility of aggregation at local points, and accelerates the convergence speed of the algorithm. The optimal variant HTSHOt2 was selected by validation on the CEC‐2022. Finally, HTSHOt2 was applied to small‐scale and large‐scale cloud computing task scheduling scenarios for the first time, achieving coordinated optimization of task completion time, resource utilization rate, and energy consumption. The results show that the total cost of HTSHOt2 in small‐scale scenarios ranges from 2.28E‐01 to 2.62E‐01, reducing by 12.37% to 24.25%. The total cost range in large‐scale scenarios is between 2.75E‐01 and 2.87E‐01, reducing by 4.33% to 7.72%. In addition, HTSHOt2 spends very little on price cost, load cost, and time cost, all of which are far superior to other comparison algorithms. This indicates that HTSHOt2 can effectively solve the problem of task scheduling optimization in cloud computing systems and has powerful performance.