Historical Location Information Based Improved Sparrow Search Algorithm for Microgrid Optimal Dispatching

The sparrow search algorithm (SSA), as an efficient meta-heuristic algorithm, has been widely used on practical problems in various fields. Nevertheless, the basic SSA is prone to fall into local optimum, which weakens the optimization ability. In order to address this problem, a novel improved SSA, called the historical location information based sparrow search algorithm (HLI-SSA), is presented. In order to solve the problem that the original sparrow search algorithm will miss part of the information during the iteration process, the historical useful information is fully utilized by creating a memory bank, which can make more population information available to individual sparrows. In addition, the Lévy stable distribution strategy is applied to improve the ability of jumping out of the local optimum. The adaptive quadratic interpolation mechanism and the use of randomness are introduced to enhance the algorithm diversity. The proposed HLI-SSA is then validated on the CEC benchmark functions. The experimental results indicate that the HLI-SSA can improve the optimization performance of the basic SSA. Finally, the method is successfully employed to the microgrid optimal dispatching problem under extreme conditions.