Particle swarm optimization (PSO) is a stochastic population-based algorithm that is designed for real-parameter optimization problems. PSO is simple and powerful algorithm, and is applied to many real world problems. However, because the bias of the search area exists in the conventional PSO, the search performance is deteriorated in non-separable problems. In order to overcome this problem, standard particle swarm optimization 2011 (SPSO2011) was proposed. The performance of SPSO2011 is not affected by the dependencies among variables. In this article, we clarify that SPSO2011 performance is affected by the distribution of the center of the search range. Also, we clarify that the global search ability fades away by the update rule of the center. Therefore, we propose a novel update rule to improve the global search ability. We clarify the effectiveness of the proposed method by numerical experiments by using CEC2005 benchmark functions.