Ant　colony　optimization　(ACO)　algorithm　is　a　metaheuristic　and　stochastic　search　technology,　which　has　been　one　of　the　effective　tools　for　solving　discrete　optimization　problems.　The　multidimensional　knapsack　problem　(MKP)　is　a　classical　combinatorial　optimization　problem,　whose　goal　is　to　find　a　subset　of　objects　that　maximizes　the　total　profit　while　satisfying　some　resource　constraints.　There　are　many　algorithms　to　effectively　solve　MKPs,　where　ACO　algorithm　is　a　new　and　robust　method.　However,　there　are　two　bottlenecks　in　which　the　ACO　algorithm　takes　too　much　time　and　solves　imprecisely　for　large-scaled　MKP.　In　this　paper,　a　novel　ACO　algorithm　with　high　performance　is　proposed,　which　syncretizes　the　improvements　of　the　pheromone　updating　and　the　stochastic　search　scheme.　Firstly,　in　light　of　the　preference　to　better　solutions,　the　association　distances　among　objects　are　mined　in　each　cycle　with　a　Top-k　strategy.　Then　a　pheromone　diffusion　model　based　on　information　fountain　of　an　object　is　established,　which　strengthens　the　collaborations　among　ants.　Finally,　a　simple　mutation　scheme　is　employed　to　optimize　the　evolution　results　of　each　step.　The　experimental　results　on　the　benchmark　testing　set　of　MKPs　show　that　the　proposed　algorithm　can　not　only　get　much　more　optimal　solutions　but　also　greatly　enhance　convergence　speed　compared　with　the　latest　improved　algorithms.