# Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing
## 2 Related Work
The algorithm task completion efficiency is good, but the task scheduling quality is difficult to guarantee. In [14], the authors presented a particle swarm optimization with the random forest classifier algorithm, which is used to solve the load balancing problem of virtual machines. In order to balance the utilization of virtual machine resources, the total task working time on a virtual machine is taken as the optimization objective. However, this method relies too much on intermediate nodes. In [15], a cloud computing resource scheduling method based on a parallel genetic algorithm was proposed. This method can reduce the overall execution time of scheduling tasks to a certain extent, but it easily falls into local solutions. In [16], the authors introduced the concept of virtual machine relative fitness according to the performance of virtual machine resources in a cloud environment, which makes it possible for virtual machine resources with a high virtual machine relative fitness to obtain greater variation and can thus speed up the convergence of the algorithm. The comparison of the related work references are shown in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial** & **Reference** & **Author** & **Algorithm Name** & **Advantages** & **Disadvantages** \\ \hline
1 & [4] & [PERSON] & Proposed a new approach for solving the shift minimization personnel task scheduling problem. & Create an efficient solution procedure. & Do not confer benefits from the perspective of system load balancing. \\ \hline
2 & [5] & [PERSON] & Presented a novel distributed implementation of Multiple Hypothesis Tracking (MHT). & Can enable fast operations on local trees and also allow sharing of hypotheses between local and remote nodes. & Do not confer benefits from the perspective of system load balancing. \\ \hline
3 & [6] & [PERSON] & Proposed a general algorithm for fast estimation of probability. & Can reduce to a single simulation run that can estimate. & Do not confer benefits from the perspective of system load balancing. \\ \hline
4 & [7] & [PERSON] & Presented a multi-objective task scheduling algorithm based on the fusion of a genetic algorithm and a particle swarm algorithm. & It can improve the global search ability and convergence speed. & Do not confer benefits from the perspective of system load balancing. \\ \hline
5 & [8] & [PERSON] & Presented an optimized algorithm for task scheduling based on genetic simulated annealing algorithm in cloud computing and its implementation. & Algorithm considers the QoS requirements of different type tasks. & Do not confer benefits from the perspective of system load balancing. \\ \hline
6 & [9] & [PERSON] & Proposed a hybrid algorithm based on ant colony optimization (ACO) and Cuckoo. & It was used to reduce task execution time. & Do not confer benefits from the perspective of system load balancing. \\ \hline
7 & [10] & [PERSON] & Proposed a new heuristic algorithm combined with the particle swarm optimization (PSO) algorithm & It has the characteristics of strong optimization search ability, fast convergence speed. & Do not confer benefits from the perspective of system load balancing. \\ \hline
8 & [11] & [PERSON] & Proposed a task scheduling strategy that improves the greedy algorithm. & It can improve the overall scheduling efficiency. & Do not confer benefits from the perspective of system load balancing. \\ \hline
9 & [12] & [PERSON]. [PERSON], [PERSON]. & Proposed an algorithm for the task scheduling problems in various heterogeneous virtual machines. & In the research of task scheduling algorithms aimed at load balancing. & The task scheduling is not efficient. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The comparison of the relevant work references.