The　Rectilinear　Steiner　Minimal　Tree　(RSMT)　problem　is　an　NP-hard　problem,　which　is　one　of　the　key　problems　in　VLSI/ULSI　physical　design.　Particle　Swarm　Optimization　(PSO)　has　been　proved　to　be　an　efficient　intelligent　algorithm　for　optimization　designs.　This　paper　presents　a　RSMT　algorithm　based　on　discrete　PSO　(DPSO),　namely　BRRA-DPSO,　to　minimize　the　wiring　length　and　reduce　the　number　of　bends,　which　is　helpful　for　via　reduction　and　reliability　increment　in　the　routing　phase.　In　order　to　solve　the　problem　of　the　slow　convergence　rate　of　PSO　used　for　a　high-dimensional　space　optimization,　a　self-adapting　strategy　that　can　adjust　the　learning　factors,　and　combine　with　the　crossover　and　mutation　operators　of　Genetic　Algorithm　(GA)　is　proposed.　The　experimental　results　show　that　the　proposed　algorithm　can　efficiently　provide　the　solution　of　RSMT　problem　with　good　quality　and　converge　more　rapidly　than　GA.　Moreover,　the　algorithm　can　also　reduce　the　number　of　bends.　©　2011　IEEE.