High-dimensional　data　analysis　and　its　great　chances　of　overfitting　result　in　great　challenges　for　constructing　efficient　models　in　practical　applications.　To　overcome　these　problems　swarm　intelligence　algorithms　can　be　utilized.　However,　the　balance　between　global　and　local　search　throughout　the　course　of　a　run　is　critical　to　the　success　of　an　intelligence　optimization　algorithm.　Moreover,　almost　all　the　available　algorithms　are　still　having　issues　like　premature　convergence　to　local　optimum　and　slow　convergence　rate,　especially　in　high-dimensional　space.　As　motivated　above,　a　new　hybrid　optimization　algorithm　integrating　particle　swarm　optimization(PSO)　with　gravitational　search　algorithm(GSA)　is　presented　(denoted　as　PSOGSA).　Based　on　the　analysis　of　the　compensatory　advantages　of　the　PSO　and　the　GSA,　in　this　paper,　we　integrate　the　ability　of　exploitation　in　PSO　with　the　ability　of　exploration　in　GSA　to　update　velocity　equations.　To　update　position　equations　a　mobility　factor　is　used　which　is　guided　by　diversity　of　population　to　improve　the　final　accuracy　and　the　convergence　speed　of　the　PSOGSA.　We　also　apply　proposed　algorithm　to　the　cluster　analysis　of　microarray　data.　Experiments　are　conducted　on　six　benchmark　test　functions,　four　artificial　data　sets　and　three　microarray　data　sets,　and　the　results　demonstrate　that　the　proposed　algorithm　possess　better　robustness.