A　novel　hybrid　particle　swarm　optimization　variant　is　proposed,　which　combines　particle　swarm　optimization　with　Gaussian　mutation　operation　based　on　random　strategy.　It　applies　Gaussian　mutation　on　the　positions　of　some　randomly　selected　particles　to　enhance　the　search　accuracy　and　convergence　speed　of　swarm.　The　proposed　algorithm　can　retain　the　diversity　of　population　and　improve　the　ability　of　global　search.　A　suite　of　benchmark　test　functions　is　employed　to　evaluate　the　performance　of　the　proposed　method.　The　results　have　been　compared　with　three　state-of-the-art　particle　swarm　optimization　variants.　Experimental　results　show　that　the　Gaussian　mutation　and　the　random　select　strategy　help　the　proposed　algorithm　to　achieve　faster　convergence　rate　and　provide　better　solutions　in　most　of　the　problems.　Further,　the　new　algorithm　has　been　tested　on　the　high-dimensional　problems.　The　results　show　that　the　proposed　algorithm　is　not　sensitive　to　high-dimensional　problems　and　can　even　give　a　better　performance.　Moreover,　the　sensitivity　analysis　of　the　parameters　was　carried　out　and　the　setting　of　the　parameters　was　given.　©　The　Author(s)　2017.