This　paper　aims　to　develop　a　surrogate　model　for　dynamics　analysis　of　a　magnetorheological　damper　(MRD)　in　the　semi-active　seat　suspension　system.　An　improved　fruit　fly　optimization　algorithm　(IFOA)　which　enhances　the　global　search　capability　of　the　original　FOA　is　proposed　to　optimize　the　structure　of　a　back　propagation　neural　network　(BPNN)　in　establishing　the　surrogate　model.　An　MRD　platform　was　fabricated　to　generate　experimental　data　to　feed　the　IFOA-BPNN　model.　Intrinsic　patterns　about　the　MRD　dynamics　behind　the　datasets　have　been　discovered　to　establish　a　reliable　MRD　surrogate　model.　The　outputs　of　the　surrogate　model　demonstrate　satisfactory　dynamics　characteristics　in　consistence　with　the　experimental　results.　Moreover,　the　performance　of　the　IFOA-BPNN　based　surrogate　model　was　compared　with　that　produced　by　the　BPNN　based,　genetic　algorithm-BPNN　based,　and　FOA-BPNN　based　surrogate　models.　The　comparison　result　shows　better　tracking　capacity　of　the　proposed　method　on　the　hysteresis　behaviors　of　the　MRD.　As　a　result,　the　newly　developed　surrogate　model　can　be　used　as　the　basis　for　advanced　controller　design　of　the　semi-active　seat　suspension　system.