Randomized directed neighborhoods with edge migration in particle swarm optimization

A key feature of particle swarm optimization algorithms is that fitness information shared with individuals in a particle´s neighborhood. The kind of neighborhood structure that is used affects the rate at which information is disseminated throughout the population. Existing work has studied global and simple local topologies, as well as more complex, but fixed neighborhood structures. This paper looks at randomly generated, directed graph structures in which information flows in one direction only, and also outgoing edges randomly migrate from one source node to another. Experimental evidence indicates that this random dynamic topology, when used with an inertia weight PSO, performs competitively with some existing methods and outperforms others.