中文摘要：

为了求解离散空间中的最优化问题,提出了一种二进制蝙蝠算法,并引入时变惯性因子来提高算法的全局收敛速度;在此基础上,为提高求解0-1背包问题时找到最优解的机率,利用贪心优化策略对无效的蝙蝠个体进行优化,从而给出了贪心二进制蝙蝠算法（GBBA）。仿真计算结果表明,GBBA算法在寻优能力和收敛性能方面比已有的GMBA算法都更优越。

英文摘要：

For solving the optimization problem in discrete space, a Binary Bat Algorithm （BBA） is proposed, and time-varying inertia factor is introduced to improve the global convergence speed of the algorithm. In order to increase the probability of finding the optimal solution in solving 0-1 knapsack problem, greedy strategy is used in the algorithm, thus a Greedy Binary Bat Algorithm （GBBA） is proposed. Simulations show that the proposed algorithm is much superior to GMBA algorithm in searching capability and convergence performance.