In order to overcome the disadvantages of social spider optimization（SSO）algorithm such as easily falling into local optima and slow convergence speed

an improved SSO algorithm integrated with searching mechanisms of differential evolution（DE）and particle swarm optimization（PSO）is proposed.The differential mutation operator of DE is introduced in the social spider optimization algorithm to allow some female spiders to mutate under the guidance of the global optimal individual and two randomly chosen individuals in the population.This mutation strategy can increase the diversity of the population

improve the ability to jump out of local optima

and accelerate the convergence speed of the algorithm.In addition

based on the above improvements and inspired by the searching mechanism of PSO

a set of dynamic nonlinear inertial weights and learning factors are added to the updating equations to get better balance between the local and global search abilities of the algorithm.The experimental results show that the improved social spider optimization algorithm has faster convergence speed and better solution precision.