Particle Swarm Optimisation with Gradually Increasing Directed Neighbourhoods (original) (raw)

Particle swarm optimisation (PSO) is an intelligent random search algorithm, and the key to success is to effectively balance between the exploration of the solution space in the early stages and the exploitation of the solution space in the late stages. This paper presents a new dynamic topology called "gradually increasing di- rected neighbourhoods (GIDN)" that provides an effective way to balance between exploration and exploitation in the entire iteration process. In our model, each particle begins with a small number of connections and there are many small isolated swarms that im- prove the exploration ability. At each iteration, we gradually add a number of new connections between particles which improves the ability of exploitation gradually. Furthermore, these connections among particles are created randomly and have directions. We for- malise this topology using random graph representations. Experi- ments are conducted on 31 benchmark test functions to validate our proposed topology. The results show that the PSO with GIDN per- forms much better than a number of the state of the art algorithms on almost all of the 31 functions.