Hardware/software partitioning (HW/SW) is a significant problem in hardware-software co-design, and it is also an NP-hard problem. In order to solve the HW/SW quickly and effectively by evolutionary algorithms, the HW/SW is firstly regarded as a variant of knapsack problem. Based on a new greedy strategy, a greedy repair and optimization algorithm GROM is proposed to eliminate the infeasible solutions. Subsequently, a general algorithm framework based on discrete evolutionary algorithm for HW/SW problem is proposed. On the basis of the above algorithm framework, genetic algorithm (GA), binary particle swarm optimization (BPSO), binary differential evolution algorithm with hybrid encoding (HBDE) and group theory-based optimization algorithm (GTOA) are used to solve large-scale HW/SW instances. The feasibility and effectiveness of the algorithm framework proposed in the paper are verified by comparing the good and bad of the calculation results of above algorithms, and pointed out that the performance of GTOA and BPSO is better than that of HBDE and GA, they are more suitable for solving large-scale HW/SW problem.

硬件/软件分区（HW/SW）是硬件-软件协同设计中的一个重要问题，也是一个NP-hard问题。为了通过进化算法快速有效地求解HW/SW，HW/SW首先被视为背包问题的一种变体。基于一种新的贪婪策略，提出了一种贪婪修复和优化算法GROM来消除不可行解。随后，提出了一种基于离散进化算法的硬件/软件问题通用算法框架。在上述算法框架的基础上，采用遗传算法（GA）、二元粒子群优化（BPSO）、混合编码二元差分进化算法（HBDE）和基于群论的优化算法（GTOA）解决大规模硬件/软件实例。