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Swarm intelligence heuristic algorithm is used to solve discounted {0-1} knapsack problem (D{0-1} KP). In order to improve the solution efficiency and quality, a repair and optimization strategy is needed to convert abnormal coding individuals into coding individuals that meet the solution constraints. On the basis of introducing the concept of group value density, taking particle swarm optimization algorithm (PSO) as an example, a set of greedy repair and optimization methods based on group (GGROA) is proposed, and the PSO based GGROA for solving D{0-1}KP algorithm (PSO-GGRDKP) is further constructed to explore the feasibility and performance of GGROA. PSO-NGROADKP and PSO-GRDKP are PSO algorithms based on item greedy repair and optimization method. The experimental results on D {0-1} KP standard data set show that compared with PSO-NGROADKP and PSO-GRDKP, PSO-GGRDKP has slightly higher error rate, but the time performance of the algorithm is improved by 13.8% and 12.9% respectively. [ABSTRACT FROM AUTHOR]