Hybrid particle swarm optimization algorithm based on arena competitive learning strategy

Abstract

The research on optimization algorithms has kept pace with the times, and one of the hot spots that has attracted much attention is the improvement of Particle Swarm Optimization algorithm (PSO) in the field of swarm intelligence. In this paper, a hybrid PSO algorithm using Adaptive Learning strategy (ALPSO) is introduced, and a hybrid PSO based on Arena Competitive Learning strategy (ACLPSO) is proposed from the perspective of search space and candidate particle generation, effectively enriches population diversity, expands the initial particle swarm search space, and improves convergence speed and accuracy.