The　particle　swarm　optimization　algorithm　(PSO)　has　successfully　been　applied　to　many　engineering　optimization　problems.　However,　the　most　of　existing　improved　PSO　algorithms　work　well　only　for　small-scale　problems　on　low-dimensional　space.　In　this　new　self-adaptive　PSO,　a　special　function,　which　is　defined　in　terms　of　the　particle　fitness,　swarm　size　and　the　dimension　size　of　solution　space,　is　introduced　to　adjust　the　inertia　weight　adaptively.　In　a　given　generation,　the　inertia　weight　for　particles　with　good　fitness　is　decreased　to　accelerate　the　convergence　rate,　whereas　the　inertia　weight　for　particles　with　inferior　fitness　is　increased　to　enhance　the　global　exploration　abilities.　When　the　swarm　size　is　large,　a　smaller　inertia　weight　is　utilized　to　enhance　the　local　search　capability　for　fast　convergence　rate.　If　the　swarm　size　is　small,　a　larger　inertia　weight　is　employed　to　improve　the　global　search　capability　for　finding　the　global　optimum.　For　an　optimization　problem　on　multi-dimension　complex　solution　space,　a　larger　inertia　weight　is　employed　to　strengthen　the　ability　to　escape　from　local　optima.　In　case　of　small　dimension　size　of　solution　space,　a　smaller　inertia　weight　is　used　for　reinforcing　the　local　search　capability.　This　novel　self-adaptive　PSO　can　greatly　accelerate　the　convergence　rate　and　improve　the　capability　to　reach　the　global　minimum　for　large-scale　problems.　Moreover,　this　new　self-adaptive　PSO　exhibits　a　consistent　methodology:　a　larger　swarm　size　leads　to　a　better　performance.　©　2008　IEEE.