This　paper　presents　a　novel　particle　swarm　optimization　(PSO)　based　on　a　non-homogeneous　Markov　chain　and　differential　evolution　(DE)　for　quantification　analysis　of　the　lateral　flow　immunoassay　(LFIA),　which　represents　the　first　attempt　to　estimate　the　concentration　of　target　analyte　based　on　the　well-established　state-space　model.　A　new　switching　local　evolutionary　PSO　(SLEPSO)　is　developed　and　analyzed.　The　velocity　updating　equation　jumps　from　one　mode　to　another　based　on　the　non-homogeneous　Markov　chain,　where　the　probability　transition　matrix　is　updated　by　calculating　the　diversity　and　current　optimal　solution.　Furthermore,　DE　mutation　and　crossover　operations　are　implemented　to　improve　local　best　particles　searching　in　PSO.　Compared　with　some　well-known　PSO　algorithms,　the　experiments　results　show　the　superiority　of　proposed　SLEPSO.　Finally,　the　new　SLEPSO　is　successfully　exploited　to　quantification　analysis　of　the　LFIA　system,　which　is　essentially　nonlinear　and　dynamic.　Therefore,　this　can　provide　a　new　method　for　the　area　of　quantitative　interpretation　of　LFIA　system.　©　2013　Elsevier　Ltd.　All　rights　reserved.