LAUSR

In this paper, an improved ant lion optimization (IALO) algorithm for parameter identification of hydraulic turbine governing system (HTGS) is proposed. In the proposed algorithm, the search space is explored by the ant lion optimization first, and then the domain is searched by the particle swarm optimization (PSO) in each iteration cycle. A chaotic mutation operation namely Logistics map is introduced for the elite to break out of the local optimum. In mutation operation, a serial-parallel combined method is developed to increase the diversity of mutant population. When the proposed IALO algorithm is applied in the parameter identification of HTGS, the comparative simulation results show that the proposed IALO algorithm has the highest accuracy among different optimization algorithms, and the proposed IALO algorithm has a good convergence characteristic and high stability.