Abstract Social Spider Optimization (SSO) algorithm is a swarm intelligence optimizationalgorithm based on the mating behavior of social spiders. Numerical simulation results have shown that SSO outperformed some classical swarm intelligence algorithms such as ParticleSwarm Optimization (PSO) algorithm and Artificial Bee Colony (ABC) algorithm and so on. However, there are still some deficiencies about SSO algorithm, such as the poor balancebetween exploration and exploitation. To this end, an improved SSO algorithm named wDESSO is proposed for global optimization, which can balance exploration and exploitation effectively.Specifically, a weighting factor changing with iteration is introduced to control and adjust the search scope of SSO algorithm dynamically. After social-spiders have completed their search,a mutation operator is then suggested for jumping out of the potential local optimization, thus can further strengthen the ability of global search. The experimental results on a set of standardbenchmark functions demonstrate the effectiveness of wDESSO in solving complex numerical optimization problems.

social-spider algorithm, swarm intelligence algorithm, global optimization, weighting factor