# A Hybrid SAO and RIME Optimizer for Global Optimization and Cloud Task Scheduling

## TL;DR

This paper introduces a new hybrid optimization algorithm for cloud task scheduling, combining SAO and RIME to improve performance and efficiency.

## Contribution

The novel hybrid SAO-RIME optimizer improves global optimization and cloud task scheduling through ecological niche initialization and enhanced escape from local optima.

## Key findings

- The HSAO algorithm outperformed 11 other algorithms on the IEEE CEC2017 test set.
- HSAO successfully improved cloud computing task scheduling with excellent practical results.

## Abstract

In a global industrial landscape where the digital economy accounts for over 40% of total output, cloud computing technology is reshaping business models at a compound annual growth rate of 19%. This trend has led to an increasing number of cloud computing tasks requiring timely processing. However, most computational tasks are latency-sensitive and cannot tolerate significant delays. This has led to the urgent need for researchers to address the challenge of effectively scheduling cloud computing tasks. This paper proposes a hybrid SAO and RIME optimizer (HSAO) for global optimization and cloud task scheduling problems. First, population initialization based on ecological niche differentiation is proposed to enhance the initial population quality of SAO, enabling it to better explore the solution space. Then, the introduction of the soft frost search strategy and hard frost piercing mechanism from the RIME optimization algorithm enables the algorithm to better escape local optima and accelerate its convergence. Additionally, a population-based collaborative boundary control method is proposed to handle outlier individuals, preventing them from clustering at the boundary and enabling more effective exploration of the solution space. To evaluate the effectiveness of the proposed algorithm, we compared it with 11 other algorithms using the IEEE CEC2017 test set and assessed the differences through statistical analysis. Experimental data demonstrate that the HSAO algorithm exhibits significant advantages. Furthermore, to validate its practical applicability, we applied HSAO to real-world cloud computing task scheduling problems, achieving excellent results and successfully completing the scheduling planning of cloud computing tasks.