Cloud-edge　hybrid　systems　can　support　delay-sensitive　applications　of　industrial　Internet　of　Things.　Edge　nodes　(ENs)　as　service　providers,　provide　users　computing/network　services　in　a　pay-as-you-go　manner,　and　they　also　suffer　from　the　high　cost　brought　by　providing　computing　resources.　Thus,　the　problem　of　profit　maximization　is　highly　important　to　ENs.　However,　with　the　development　of　5G　network　technologies,　a　large　number　of　mobile　devices　(MDs)　are　connected　to　ENs,　making　the　above-mentioned　problem　a　high-dimensional　challenge,　which　is　highly　difficult　to　solve.　This　work　formulates　a　joint　optimization　problem　of　task　offloading,　task　partitioning,　and　associations　of　large-scale　users　to　ENs　to　maximize　the　profit　of　ENs.　This　work　focuses　on　applications　that　can　be　split　into　multiple　subtasks,　each　of　which　can　be　completed　in　MDs,　ENs　and　a　cloud　data　center.　Specifically,　a　mixed　integer　nonlinear　program　is　formulated　to　maximize　ENs＇　profit.　Then,　a　novel　hybrid　algorithm　named　Genetic　Simulated-annealing-based　Particle　swarm　optimizer　with　a　Stacked　Autoencoder　(GSPSA)　is　designed　to　solve　it.　Real-life　data-based　experimental　results　demonstrate　that　compared　with　other　peer　algorithms,　GSPSA　increases　the　profit　of　ENs　while　strictly　meeting　latency　needs　of　users＇　tasks.　The　dimension　of　the　problem　that　can　be　solved　is　increased　by　more　than　50%　with　GSPSA.　　©　2022　IEEE.