For solving the knapsack problem with a single continuous variable (KPC), a binary team game algorithm (TGA) with one-way mutation strategy is proposed. Firstly, without changing the evolution mode of TGA, three basic operations are reconstructed based on modulo 2 operation. Then, a binary TGA (BTGA) suitable for solving binary optimization problem is proposed. In order to use BTGA to solve KPC problem effectively, a one-way mutation strategy to improve individual quality is subsequently developed. Finally, based on BTGA and the existing repair and optimization algorithm of eliminating infeasible solutions, a novel algorithm MOBTGA for solving KPC problem is proposed. For validating the performance of MOBTGA, Kruskal-Wallis test is used to determine the reasonable values of its parameters. The comparison of experimental results obtained by different algorithms for two sets of KPC instances is subsequently executed. The comparison results show that MOBTGA has better performance than the existing heuristic algorithms ETDE, S-HBDE and B-HBDE in terms of solution accuracy and stability for solving KPC. The same time, the solving speed of MOBTGA also has a certain degree of competitiveness. Thus, MOBTGA is a quick and efficient heuristic algorithm for solving large-scale KPC instances.

为了解决单连续变量背包问题，提出了一种具有单向变异策略的二元团队博弈算法。首先，在不改变TGA演进模式的前提下，基于模2运算重构了三个基本运算。然后，提出了一种适合解决二进制优化问题的二进制TGA（BTGA）。为了使用BTGA有效解决KPC问题，随后开发了一种单向突变策略来提高个人素质。最后，基于BTGA和现有的消除不可行解的优化算法，提出了一种解决KPC问题的新型算法MOBTGA。为了验证MOBTGA的性能，使用Kruskal-Wallis测试确定其参数的合理值。随后对两组KPC实例通过不同算法获得的实验结果进行比较。比较结果表明，MOBTGA在求解KPC的求解精度和稳定性方面比现有的启发式算法ETDE，S-HBDE和B-HBDE更好。同时，MOBTGA的求解速度也具有一定的竞争力。因此，MOBTGA是一种快速有效的启发式算法，用于解决大规模KPC实例。