Particle swarm optimization algorithm based on beetle antenna search optimization

Abstract

For the standard particle swarm optimization algorithm, when optimizing multi-dimensional extreme value functions, it is easy to fall into the problem of local optimal solution and poor optimization effect. This paper proposes a particle swarm based on beetle antennae search optimization. This algorithm introduces beetle antenna search algorithm into particle swarm optimization algorithm. Each particle first updates its position according to the particle swarm optimization algorithm, and then takes the updated position as the initial value of the beetle antenna search algorithm. After iteration, a new location is obtained. By comparing the positions before and after the iteration, the optimal value is respectively compared with the individual history optimal value and the global optimal value, and the individual history optimal value and the global optimal value are updated. Joining beetles’ optimization strategy can better jump out of the local optimal value. Through simulation analysis of three different functions, it is concluded that the BASPSO algorithm has better optimization effect and better robustness.