Multi-strategy Sparrow Search Algorithm based on Differential Evolution

For the purpose of improving the sparrow search algorithm, there are some disadvantages of low convergence accuracy, slow convergence speed and prone to get bogged down local optimization, a multi-strategy sparrow search algorithm is proposed in this paper. Firstly, Initializing the sparrow population using the chaotic opposition-based learning strategy, which raise the randomness of sparrow individuals and make the initial solution more uniformly distributed in the solution space. Secondly, the location updating formula of discoverer is improved, in which nonlinear weight factor and Cauchy operator are adopted to balance the global exploration and local exploitation ability of this algorithm. Finally, according to the idea of grey wolf optimizer and differential evolution algorithm, the individuals of the population are disturbed in stages to further improve the diversity of the population and reduce the possibility of the algorithm getting caught in the local extremum. Through experiments on 13 selected test functions, the simulation results prove that the proposed algorithm has better convergence speed and optimization accuracy than the original algorithm.