In order to improve the samples diversity and convergence properties of social spiders optimization algorithm (SSO), an adaptation social spider optimization algorithm based on dynamic multi-swarm strategy (DMASSO) is proposed. According to the algorithm samples diversity and evolutionary level, the spider population is dynamically divided into different sizes leading groups and supporting groups, and the adaptive learning factor and Gaussian disturbance factor are introduced to improve the algorithm update ways, which helps to improve the algorithm global optimization ability and maintain the diversity of the sample population. For the test results of typical characteristics functions show that compared to SSO algorithm, SFLA algorithm and other optimization algorithms, the new algorithm has better convergence speed and convergence accuracy.
