Dynamic Topologies for Particle Swarms

The Particle Swarm Optimization (PSO) algorithm is a population-based metaheuristics in which the individuals communicate through decentralized networks. The network can be of many forms but traditionally its structure is predetermined and remains fixed during the search. This paper investigates an alternative approach. The particles are positioned on a 2-dimensional grid of nodes. During the run, they move through the network according to simple rules, while interacting with each other using signs that they leave on the nodes. The links between the particles – and consequently the information flow – are then defined at each time step by the position of the particle on the grid. As a result, each particle’s set of neighbors and connectivity degree varies during the search progress. The particles can move randomly or instead track signs left by other particles on the grid. In this paper, after a formal description of the general model, two different strategies (random and sign-based) are tested and compared to standard topologies on unimodal and multimodal functions, including a rotated and a shifted function with noise from the CEC benchmark. The experiments demonstrate that the dynamics provided by the proposed structure results in a more consistent and stable performance throughout the test set. The working mechanisms of the model are simple and easy to implement.