Particle Swarm Optimization with Disagreements on Stagnation

摘要

This paper introduces a new family of particle swarm optimizers that model social disagreements among particles when stagnation occurs inside the swarm. After a short introduction into particle swarm optimization, we describe theoretically the new concept and define a special type of particle swarm optimization with disagreements that generates riots on stagnation. After testing it on several popular optimization benchmarks, we conclude that it can help any PSO escape stagnation. This work illustrates one of the many benefits of using the particle swarm optimization with disagreements.