One of our most recent research lines deals with network topologies for the Particle Swarm Optimization algorithm (PSO). We have been studying dynamic and partially connected topologies and the first results are reported in two papers recently published in CEC and GECCO: Partially Connected Topologies for Particle Swarm (GECCO) and A Study on Time-Varying Partially Connected Topologies for the Particle Swarm (CEC). We have concluded that a random and dynamic partially connected grid topology with von Neumann neighborhood is able to perform more consistenly than tradicional configurations. This is the abstract of the paper published in the CEC proceedings:
This paper presents a study on the effects of dynamic and partially connected 2-dimensional topologies on the performance of the particle swarm optimization (PSO). The swarm is positioned on 2-dimensional grids of nodes and the particles move through the nodes according to a simple rule. Meanwhile, the von Neumann neighborhood is used to decide which particles influence each individual. Structures with growing size are tested on a classical benchmark and compared to several configurations such as lbest, gbest and the standard von Neumann configuration. The results show that the partially connected grids with von Neumann neighborhood structure performs more consistently when compared to lbest, gbest and the standard von Neumann topology.