Acceleration　coefficients　controlled　the　impact　of　the　particle＇s　own　experiences　and　the　other　particles＇　experiences　on　the　trajectory　of　each　particle.　The　setting　of　acceleration　played　a　key　role　in　the　performance　of　particle　swarm　optimization.　To　efficiently　control　the　local　search　and　convergence　to　the　global　optimum　solution,　a　good　investigation　to　the　key　role　of　the　setting　of　acceleration　coefficients　was　made.　A　new　recommended　value　for　acceleration　coefficients　and　corresponding　PSO　algorithm　were　presented　basing　on　the　investigation.　In　the　new　automation　strategy,　an　unsymmetrical　transfer　range　of　acceleration　coefficients　were　considered　and　it　obtained　preferable　effects　when　changing　c1　from　2.75　to　1.25　and　changing　c2　from　0.5　to　2.25.　Four　benchmark　functions　were　selected　as　test　functions.　All　four　functions　were　tested　with　different　dimensions　and　different　population　sizes.　The　experimental　results　illustrate　that　the　new　method　of　setting　acceleration　coefficients　speeds　up　the　convergence　of　PSO　and　improves　the　performance　of　PSO.　Furthermore,　it　also　can　reserve　more　diversity　of　the　swarm　at　the　beginning　period　of　the　algorithm　and　thus　have　more　ability　to　escape　from　local　minimum.　©2006　IEEE.