In　the　practical　industrial　field,　optimizing　parameter　configurations　during　factory　operations　is　an　indispensable　step,　especially　for　multi-objective　optimization　problems　(MOPs)　that　involve　high　computational,　economic　costs　or　extensive　numerical　simulations,　which　was　categorized　as　Expensive　Multi-Objective　Optimization　Problems　(EMOPs).　To　efficiently　and　reasonably　evaluate　fitness　in　the　face　of　EMOP,　it　is　essential　to　develop　a　surrogate　model　to　minimize　costly　expensive　function　evaluations　and　assist　the　entire　optimization　process.　This　study　proposes　a　Surrogate-Assisted　Evolutionary　Algorithm　SFEA　that　integrates　Support　Vector　Machines　(SVM)　and　Feedforward　Neural　Networks　(FNN).　SFEA　constructs　surrogate　models　using　heterogeneous　integration　methods　to　mitigate　the　limitations　of　a　single-model　approaches　in　complex　and　variable　optimization　scenarios,　thereby　enhancing　the　efficiency　and　quality　of　the　optimization　outcomes.　Additionally,　this　paper　proposes　an　adaptive　sampling　strategy　and　a　corresponding　sample　filling　mechanism　within　a　dual-　archive　management　framework,　based　on　the　convergence　and　diversity　indices　of　the　algorithm.　Experimental　validation　on　multi-objective　DTLZ　and　WFG　benchmark　problems　confirms　the　effectiveness　and　feasibility　of　SFEA　in　addressing　expensive　multi-objective　optimization　challenges.