A　robust　hybrid　genetic　algorithm　which　can　be　used　to　solve　process　synthesis　problems　with　Mixed　Integer　Nonlinear　Programming　(MINLP)　models　is　developed.　The　proposed　hybrid　approach　constructs　an　efficient　genetic　simulated　annealing　(GSA)　algorithm　for　global　search,　while　the　iterative　hill　climbing　(IHC)　method　as　a　local　search　technique　is　incorporated　into　GSA　loop　to　speed　up　the　convergence　of　the　algorithm.　In　order　to　efficiently　locate　quality　solution　to　complex　optimization　problem,　a　self-adaptive　mechanism　is　developed　to　maintain　a　tradeoff　between　the　global　and　local　search.　The　computational　results　indicate　that　the　global　searching　ability　and　the　convergence　speed　of　this　hybrid　algorithm　are　significantly　improved.　Further,　the　proposed　algorithm　is　tailored　to　find　optimum　solution　to　HENS　problem,　The　results　show　that　the　proposed　approach　could　provide　designers　with　a　least-cost　HEN　with　less　computational　cost　comparing　with　other　optimization　methods.　©　2011　Zhejiang　University.