Given　the　complexity　of　frozen　soil　mechanics,　establishing　constitutive　models　that　reasonably　de-scribe　its　mechanical　behavior　inevitably　requires　increasing　the　number　of　model　parameters.　The　predictive　ac-curacy　of　these　models　largely　depends　on　the　rational　determination　of　these　parameters.　However,　some　pa-rameters　in　frozen　soil　constitutive　models　cannot　be　directly　determined　through　experiments　or　empirical　equa-tions.　Therefore,　parameter　identification　of　frozen　soil　constitutive　models　based　on　limited　experimental　data　holds　significant　engineering　significance.　Optimization　algorithms　provide　effective　tools　for　parameter　identifi-cation　in　various　engineering　fields,　and　their　application　in　geotechnical　engineering　has　become　increasingly　widespread.　Among　them,　genetic　algorithm(GA)and　particle　swarm　optimization(PSO)algorithm　are　two　popular　optimization　algorithms.　However,　both　algorithms　have　their　own　advantages　and　disadvantages.　GA　lacks　target　orientation,　but　possesses　strong　global　search　capability,　while　PSO　is　prone　to　local　optima,　but　efficient　information　transmission.　Therefore,　this　paper　proposes　a　novel　hybrid　algorithm,　the　GA-PSO　algo-rithm,　which　combines　the　strengths　of　GA　and　PSO　while　mitigating　their　respective　weaknesses.　In　the　GA-PSO　algorithm,　the　incorporation　of　the　elite　preservation　strategy　within　the　GA　computation　step　serves　as　ad-vantageous　information　for　the　PSO　computation　step,　preventing　PSO　from　getting　stuck　in　local　optima.　Con-versely,　the　incorporation　of　the　non-elite　optimization　strategy　into　the　PSO　computation　step　provides　guiding　information　for　the　GA　computation　step　to　address　the　issue　of　lacking　target　orientation　in　the　GA　algorithm.　The　specific　process　involves　global　exploration　of　the　solution　space　using　GA　calculation　step　while　preserving　elite　individuals,　followed　by　further　optimization　of　poor　fit　individuals　using　PSO　calculation　steps.　Validation　results　based　on　two　standard　test　functions,　i.　e.,　Griewank　function　and　Restrigin　function,　illustrate　that　the　GA-PSO　algorithm　exhibits　superior　global　search　capability　and　faster　convergence　speed　in　the　solution　space.　Furthermore,　The　GA-PSO　algorithm　is　applied　to　the　parameter　identification　of　the　non-orthogonal　elastoplas-tic　constitutive　model　for　frozen　soil.　The　results　of　model　parameter　identification,　as　well　as　the　comparison　and　validation　of　model　prediction　with　test　results,　indicate　that　the　GA-PSO　algorithm　is　proficient　in　effective-ly　identifying　parameters　of　the　non-orthogonal　elastoplastic　constitutive　model　for　frozen　soil,　thereby　enhanc-ing　the　predictive　accuracy　of　the　model.　©　2024　Science　China　Press.　All　rights　reserved.