Particle　swarm　optimization　(PSO),　as　a　kind　of　swarm　intelligence　algorithm,　has　the　advantages　of　simple　algorithm　principle,　less　programmable　parameters　and　easy　programming.　Many　scholars　have　applied　particle　swarm　optimization　(PSO)　to　various　fields　through　learning　it,　and　successfully　solved　linear　problems,　nonlinear　problems,　multiobjective　optimization　and　other　problems.　However,　the　algorithm　also　has　obvious　problems　in　solving　problems,　such　as　slow　convergence　speed,　too　early　maturity,　falling　into　local　optimization　in　advance,　etc.,　which　makes　the　convergence　speed　slow,　search　the　optimal　value　accuracy　is　not　high,　and　the　optimization　effect　is　not　ideal.　Therefore,　many　scholars　have　improved　the　particle　swarm　optimization　algorithm.　Taking　into　account　the　improvement　ideas　proposed　by　scholars　in　the　early　stage　and　the　shortcomings　still　existing　in　the　improvement,　this　paper　puts　forward　the　idea　of　improving　particle　swarm　optimization　algorithm　in　the　future.　©　2020　IEEE.