In　order　to　solve　the　large-scale,　strong　coupling,　and　nonlinear　optimization　problems　in　many　Internet-of-Things　(IoT)　applications,　such　as　intelligent　infrastructure　and　smart　city,　this　article　proposes　a　real-coded　genetic　algorithm　based　on　an　auxiliary-individual-directed　crossover　operator　(AIDX).　AIDX　is　an　alternative　offspring　framework　of　the　directed　crossover　which　uses　auxiliary　individual　to　reduce　the　search　space　of　alternative　offspring　for　the　rapid　optimization　of　multidimensional　problems.　In　our　solution　AIDX,　the　parents-center　distribution　is　adopted　to　reduce　the　risk　of　convergence　to　local　optimization　and　enhance　the　stability　of　the　algorithm.　In　addition,　in　order　to　increase　the　diversity　of　the　population　at　the　late　stage　of　optimization,　K-Bit-Swap　(KBS)　is　used　as　a　supplement　for　the　exchange　of　genetic　information　among　individuals　in　different　dimensions.　In　the　extensive　experiments,　24　benchmarks　with　different　dimensions　are　used　to　evaluate　the　performance　of　AIDX-GA.　The　results　show　that　the　proposed　AIDX-GA　has　a　significantly　improved　optimization　effect　on　multidimensional　optimization　problems,　and　the　stability　of　the　algorithm　is　largely　enhanced　even　when　the　global　optimum　is　located　near　the　boundary.　AIDX-GA　has　also　been　evaluated　in　an　industrial　IoT　case　that　identifies　the　resistance　coefficients　of　a　pipe　network　in　the　city　infrastructure.　The　results　show　that　the　accuracy　of　AIDX-GA　is　high,　and　it　has　excellent　universality　and　stability,　which　can　be　used　to　solve　many　IoT　problems.