Very　large　scale　integration　(VLSI)　circuit　partitioning　is　an　important　problem　in　design　automation　of　VLSI　chips　and　multichip　systems;　it　is　an　NP-hard　combinational　optimization　problem.　In　this　paper,　an　effective　hybrid　multi-objective　partitioning　algorithm,　based　on　discrete　particle　swarm　optimzation　(DPSO)　with　local　search　strategy,　called　MDPSO-LS,　is　presented　to　solve　the　VLSI　twoway　partitioning　with　simultaneous　cutsize　and　circuit　delay　minimization.　Inspired　by　the　physics　of　genetic　algorithm,　uniform　crossover　and　random　two-point　exchange　operators　are　designed　to　avoid　the　case　of　generating　infeasible　solutions.　Furthermore,　the　phenotype　sharing　function　of　the　objective　space　is　applied　to　circuit　partitioning　to　obtain　a　better　approximation　of　a　true　Pareto　front,　and　the　theorem　of　Markov　chains　is　used　to　prove　global　convergence.　To　improve　the　ability　of　local　exploration,　Fiduccia-Matteyses　(FM)　strategy　is　also　applied　to　further　improve　the　cutsize　of　each　particle,　and　a　local　search　strategy　for　improving　circuit　delay　objective　is　also　designed.　Experiments　on　ISCAS89　benchmark　circuits　show　that　the　proposed　algorithm　is　efficient.