WiMi Hologram Cloud Inc. announced that it optimized cloud task scheduling using group intelligence algorithms. A group intelligence algorithm is a computational method based on the behavior of groups in nature, which can demonstrate powerful search and optimization capabilities in solving complex problems by simulating the interactions and collaborations of individuals in a group. Using group intelligence algorithms to solve cloud task scheduling problems can improve task execution efficiency and resource utilization. Group intelligence algorithms are a class of optimization algorithms that simulate the behavior of groups of organisms in nature, such as ant colony algorithms and particle swarm algorithms. These algorithms find the global optimal solution by simulating the collaboration and competition mechanism of biological groups.

In cloud task scheduling, the use of population intelligence algorithms can view tasks and resources as individuals in a group, and find the optimal task scheduling solution through collaboration and competition among individuals. This can fully utilize the resources in the system, improve the task execution efficiency, reduce the waiting time, and lower the energy consumption and cost of the system. Cloud task scheduling using group intelligence algorithms can meet users' needs, improve the response speed of the system, reduce the cost, and improve resource utilization.

The group intelligence algorithm can be applied to different aspects of cloud task scheduling, such as task allocation, task scheduling, and task execution. For example, cloud tasks are scheduled using particle swarm optimization (PSO). The PSO algorithm simulates the flight behavior of birds in a flock by constantly adjusting the position and speed of each bird in the flock to find the optimal solution.

In cloud task scheduling, each task can be considered as a particle, the position of each particle indicates the virtual machine to which the task is assigned and the velocity indicates the execution speed of the task. By constantly updating the position and velocity of the particles, the optimal task scheduling solution can be found to improve task execution efficiency and resource utilization. The particle swarm algorithm is an optimization algorithm that simulates the foraging behavior of a flock of birds.

In cloud task scheduling, the task can be regarded as the target that needs to be foraged by the flock of birds, and the cloud computing resources are regarded as the path of the flock of birds. The particle swarm algorithm searches for the optimal task scheduling scheme by simulating the position and speed adjustment of the bird flock during the search process. Specifically, each particle represents a task allocation scheme and adjusts its position and speed according to its own historical optimal position and the flock's optimal position.

The PSO algorithm includes initializing the particle swarm, evaluating the fitness, updating the speed and position, and updating the global optimal solution and individual optimal solution.