A hybrid group search optimizer with metropolis rule

Group search optimizer (GSO) is a new novel swarm intelligent technique by simulating animal behavioral ecology. However, as a stochastic optimization algorithm, it is still easily trapped into local optima when dealing with multi-modal optimization problems. Therefore, in this paper, a new variant of GSO is designed by hybriding Metropolis rule to further enhancing the capability escaping from local optima. Simulation results show the performance of this new variant is superior to the standard group search optimizer and particle swarm optimization in multi-model problems especially for high-dimensional cases.