Abstract: Social spider optimization algorithm (SSA) is a new swarm intelligence algorithm inspired by the foraging behavior of social spiders. To address the slow convergence rate and low calculation accuracy in the later iterative period because of the fixed step size of the spiders and the decreasing attraction between each area, we propose a modified social spider optimization algorithm (MSSA). Adding the self-adaptive method ensures that the optimal step size can change adaptively in the iterative process, thereby improving the convergence. We apply the bias random walk (BRW) mechanism to strengthen the local search capability. Simulation results on some well-known benchmark functions show that MSSA has obvious advantages in terms of convergence rate and accuracy compared with SSA and other improved algorithms.