This　paper　considers　the　nonlinearly　constrained　continuous　global　minimization　problem.　Based　on　the　idea　of　the　penalty　function　method,　an　auxiliary　function,　which　has　approximately　the　same　global　minimizers　as　the　original　problem,　is　constructed.　An　algorithm　is　developed　to　minimize　the　auxiliary　function　to　find　an　approximate　constrained　global　minimizer　of　the　constrained　global　minimization　problem.　The　algorithm　can　escape　from　the　previously　converged　local　minimizers,　and　can　converge　to　an　approximate　global　minimizer　of　the　problem　asymptotically　with　probability　one.　Numerical　experiments　show　that　it　is　better　than　some　other　well　known　recent　methods　for　constrained　global　minimization　problems.　©　2008　Elsevier　B.V.　All　rights　reserved.