Particle swarm optimization with FUSS and RWS for high dimensional functions

High dimensional optimization problems play an important role in many complex engineering area. Though many variants of particle swarm optimization (PSO) have been proposed, however, most of them are tested and compared with dimension no larger than 300. Since numerical problem with high-dimension maintains a large linkage and correlation among different variables, and the number of local optimum increases significantly with different dimensions, this paper proposes a novel variant of PSO aiming to provide a balance between exploration and exploitation capability. Firstly, the fitness uniform selection strategy (FUSS) with a weak selection pressure is incorporated into the standard PSO. Secondly, “random walk strategy” (RWS) with four different form, is designed to further enhance the exploration capability to escaping from a local optimum. Finally, the proposed PSO combined with FUSS and RWS is applied to seven famous high dimensional benchmark with the dimension up to 3000. Simulation results demonstrate good performance of the new method in solving high dimensional multi-modal problems when compared with two other variants of the PSO.