Load Balancing Scheduling Algorithm Based on Improved Particle Swarm Optimization

Abstract

Aiming at the defects of the traditional particle swarm optimization algorithm, as the number of iterations increases, the flight speed of the particles in the space becomes faster, and the particles are easy to gather and close to the local position, resulting in the algorithm falling into the local optimal situation, and the situation that it cannot continue to explore the optimal solution in a larger space. Therefore, the inertial center of gravity is introduced to determine the position and state of particles. If the inertial center of gravity of particle swarm is smaller in the current iteration, the distribution of particles will be more uniform, so as to avoid the algorithm falling into local solution. The larger the inertia barycenter of particle swarm is, the looser the particle distribution is. When it reaches a certain degree, the algorithm will converge prematurely and may miss the optimal solution. Therefore, the inertia center of gravity is allowed to float within a certain range during optimization to solve the problem that the inertia weight fluctuates greatly and the algorithm converges in the early stage. In this paper, the inertia weight is analyzed and improved, so that the performance of the algorithm is further improved.