A hybrid social spider optimization algorithm with differential evolution for global optimization

Social spider optimization (sso) algorithm is a swarm intelligence optimization algorithm based on the mating behavior of social spiders. numerical simulation results have shown that sso outperformed some classical swarm intelligence algorithms such as particle swarm 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 balance between 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 standard benchmark functions demonstrate the effectiveness of wdesso in solving complex numerical optimization problems. © j.ucs.