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Premature convergence has been recognized as one of the major drawbacks of particle swarm optimization (PSO) algorithms. In particular, the lack of diversity in PSO performance is an essential cause that commonly results in high susceptibility to prematurely converge to local optima especially in complex multimodal problems with high dimensionality. This paper presents a new PSO operational strategy based on gravity concept to address the aforementioned drawback and it is named as gravity-based particle swarm optimizer (GPSO). In addition, GPSO is further modified by adopting the cooperation concept of the conventional cooperative particle swarm optimizer (CPSO) to develop an extended version of GPSO called cooperative gravity-based particle swarm optimizer (CGPSO). Simulation results manifest that CGPSO performs satisfactorily on unimodal functions while it generally performs better on …