Particle swarm optimization (PSO) is one of the practical metaheuristic algorithms which is applied for numerical global optimization‎. ‎It benefits from the nature inspired swarm intelligence‎, ‎but it suffers from a local optima problem‎. ‎Recently‎, ‎another nature inspired metaheuristic called Symbiotic Organisms Search (SOS) is proposed‎, ‎which doesn't have any parameters to set at start‎. ‎In this paper‎, ‎the PSO and SOS algorithms are combined to produce a new hybrid metaheuristic algorithm for the global optimization problem‎, ‎called PSOS‎. ‎In this algorithm‎, ‎a minimum number of the parameters are applied which prevent the trapping in local solutions and increase the success rate‎, ‎and also the SOS interaction phases are modified‎. ‎The proposed algorithm consists of the PSO and the SOS phases‎. ‎The PSO phase gets the experiences for each appropriate solution and checks the neighbors for a better solution‎, ‎and the SOS phase benefits from the gained experiences and performs symbiotic interaction update phases‎. ‎Extensive experimental results showed that the PSOS outperforms both the PSO and SOS algorithms in terms of the convergence and success ‎rates.‎